Structure-based bayesian sparse reconstruction
Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.
2012-01-01
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical
BUMPER: the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction
Holden, Phil; Birks, John; Brooks, Steve; Bush, Mark; Hwang, Grace; Matthews-Bird, Frazer; Valencia, Bryan; van Woesik, Robert
2017-04-01
We describe the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. The principal motivation for a Bayesian approach is that the palaeoenvironment is treated probabilistically, and can be updated as additional data become available. Bayesian approaches therefore provide a reconstruction-specific quantification of the uncertainty in the data and in the model parameters. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring 2 seconds to build a 100-taxon model from a 100-site training-set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training-sets under ideal assumptions. We then use these to demonstrate both the general applicability of the model and the sensitivity of reconstructions to the characteristics of the training-set, considering assemblage richness, taxon tolerances, and the number of training sites. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. In all of these applications an identically configured model is used, the only change being the input files that provide the training-set environment and taxon-count data.
Hierarchical Bayesian sparse image reconstruction with application to MRFM.
Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves
2009-09-01
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.
A Bayesian account of quantum histories
International Nuclear Information System (INIS)
Marlow, Thomas
2006-01-01
We investigate whether quantum history theories can be consistent with Bayesian reasoning and whether such an analysis helps clarify the interpretation of such theories. First, we summarise and extend recent work categorising two different approaches to formalising multi-time measurements in quantum theory. The standard approach consists of describing an ordered series of measurements in terms of history propositions with non-additive 'probabilities.' The non-standard approach consists of defining multi-time measurements to consist of sets of exclusive and exhaustive history propositions and recovering the single-time exclusivity of results when discussing single-time history propositions. We analyse whether such history propositions can be consistent with Bayes' rule. We show that certain class of histories are given a natural Bayesian interpretation, namely, the linearly positive histories originally introduced by Goldstein and Page. Thus, we argue that this gives a certain amount of interpretational clarity to the non-standard approach. We also attempt a justification of our analysis using Cox's axioms of probability theory
Theoretical evaluation of the detectability of random lesions in bayesian emission reconstruction
International Nuclear Information System (INIS)
Qi, Jinyi
2003-01-01
Detecting cancerous lesion is an important task in positron emission tomography (PET). Bayesian methods based on the maximum a posteriori principle (also called penalized maximum likelihood methods) have been developed to deal with the low signal to noise ratio in the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the prior parameters in Bayesian reconstruction control the resolution and noise trade-off and hence affect detectability of lesions in reconstructed images. Bayesian reconstructions are difficult to analyze because the resolution and noise properties are nonlinear and object-dependent. Most research has been based on Monte Carlo simulations, which are very time consuming. Building on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers, here we develop a theoretical approach for fast computation of lesion detectability in Bayesian reconstruction. The results can be used to choose the optimum hyperparameter for the maximum lesion detectability. New in this work is the use of theoretical expressions that explicitly model the statistical variation of the lesion and background without assuming that the object variation is (locally) stationary. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predications and the Monte Carlo results
Bayesian signal reconstruction for 1-bit compressed sensing
International Nuclear Information System (INIS)
Xu, Yingying; Kabashima, Yoshiyuki; Zdeborová, Lenka
2014-01-01
The 1-bit compressed sensing framework enables the recovery of a sparse vector x from the sign information of each entry of its linear transformation. Discarding the amplitude information can significantly reduce the amount of data, which is highly beneficial in practical applications. In this paper, we present a Bayesian approach to signal reconstruction for 1-bit compressed sensing and analyze its typical performance using statistical mechanics. As a basic setup, we consider the case that the measuring matrix Φ has i.i.d entries and the measurements y are noiseless. Utilizing the replica method, we show that the Bayesian approach enables better reconstruction than the l 1 -norm minimization approach, asymptotically saturating the performance obtained when the non-zero entry positions of the signal are known, for signals whose non-zero entries follow zero mean Gaussian distributions. We also test a message passing algorithm for signal reconstruction on the basis of belief propagation. The results of numerical experiments are consistent with those of the theoretical analysis. (paper)
On a full Bayesian inference for force reconstruction problems
Aucejo, M.; De Smet, O.
2018-05-01
In a previous paper, the authors introduced a flexible methodology for reconstructing mechanical sources in the frequency domain from prior local information on both their nature and location over a linear and time invariant structure. The proposed approach was derived from Bayesian statistics, because of its ability in mathematically accounting for experimenter's prior knowledge. However, since only the Maximum a Posteriori estimate was computed, the posterior uncertainty about the regularized solution given the measured vibration field, the mechanical model and the regularization parameter was not assessed. To answer this legitimate question, this paper fully exploits the Bayesian framework to provide, from a Markov Chain Monte Carlo algorithm, credible intervals and other statistical measures (mean, median, mode) for all the parameters of the force reconstruction problem.
Energy Technology Data Exchange (ETDEWEB)
Chakraborty, Shubhankar; Das, Prasanta Kr., E-mail: pkd@mech.iitkgp.ernet.in [Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India); Roy Chaudhuri, Partha [Department of Physics, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India)
2016-07-15
In this communication, a novel optical technique has been proposed for the reconstruction of the shape of a Taylor bubble using measurements from multiple arrays of optical sensors. The deviation of an optical beam passing through the bubble depends on the contour of bubble surface. A theoretical model of the deviation of a beam during the traverse of a Taylor bubble through it has been developed. Using this model and the time history of the deviation captured by the sensor array, the bubble shape has been reconstructed. The reconstruction has been performed using an inverse algorithm based on Bayesian inference technique and Markov chain Monte Carlo sampling algorithm. The reconstructed nose shape has been compared with the true shape, extracted through image processing of high speed images. Finally, an error analysis has been performed to pinpoint the sources of the errors.
Optimization of Bayesian Emission tomographic reconstruction for region of interest quantitation
International Nuclear Information System (INIS)
Qi, Jinyi
2003-01-01
Region of interest (ROI) quantitation is an important task in emission tomography (e.g., positron emission tomography and single photon emission computed tomography). It is essential for exploring clinical factors such as tumor activity, growth rate, and the efficacy of therapeutic interventions. Bayesian methods based on the maximum a posteriori principle (or called penalized maximum likelihood methods) have been developed for emission image reconstructions to deal with the low signal to noise ratio of the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the smoothing parameter of the image prior in Bayesian reconstruction controls the resolution and noise trade-off and hence affects ROI quantitation. In this paper we present an approach for choosing the optimum smoothing parameter in Bayesian reconstruction for ROI quantitation. Bayesian reconstructions are difficult to analyze because the resolution and noise properties are nonlinear and object-dependent. Building on the recent progress on deriving the approximate expressions for the local impulse response function and the covariance matrix, we derived simplied theoretical expressions for the bias, the variance, and the ensemble mean squared error (EMSE) of the ROI quantitation. One problem in evaluating ROI quantitation is that the truth is often required for calculating the bias. This is overcome by using ensemble distribution of the activity inside the ROI and computing the average EMSE. The resulting expressions allow fast evaluation of the image quality for different smoothing parameters. The optimum smoothing parameter of the image prior can then be selected to minimize the EMSE
Czech Academy of Sciences Publication Activity Database
Fernandes, R.; Millard, A.R.; Brabec, Marek; Nadeau, M.J.; Grootes, P.
2014-01-01
Roč. 9, č. 2 (2014), Art . no. e87436 E-ISSN 1932-6203 Institutional support: RVO:67985807 Keywords : ancienit diet reconstruction * stable isotope measurements * mixture model * Bayesian estimation * Dirichlet prior Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.234, year: 2014
Bayesian image reconstruction for improving detection performance of muon tomography.
Wang, Guobao; Schultz, Larry J; Qi, Jinyi
2009-05-01
Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.
Structure-based bayesian sparse reconstruction
Quadeer, Ahmed Abdul
2012-12-01
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
A Nonparametric Bayesian Approach For Emission Tomography Reconstruction
International Nuclear Information System (INIS)
Barat, Eric; Dautremer, Thomas
2007-01-01
We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM
Intensity-based bayesian framework for image reconstruction from sparse projection data
International Nuclear Information System (INIS)
Rashed, E.A.; Kudo, Hiroyuki
2009-01-01
This paper presents a Bayesian framework for iterative image reconstruction from projection data measured over a limited number of views. The classical Nyquist sampling rule yields the minimum number of projection views required for accurate reconstruction. However, challenges exist in many medical and industrial imaging applications in which the projection data is undersampled. Classical analytical reconstruction methods such as filtered backprojection (FBP) are not a good choice for use in such cases because the data undersampling in the angular range introduces aliasing and streak artifacts that degrade lesion detectability. In this paper, we propose a Bayesian framework for maximum likelihood-expectation maximization (ML-EM)-based iterative reconstruction methods that incorporates a priori knowledge obtained from expected intensity information. The proposed framework is based on the fact that, in tomographic imaging, it is often possible to expect a set of intensity values of the reconstructed object with relatively high accuracy. The image reconstruction cost function is modified to include the l 1 norm distance to the a priori known information. The proposed method has the potential to regularize the solution to reduce artifacts without missing lesions that cannot be expected from the a priori information. Numerical studies showed a significant improvement in image quality and lesion detectability under the condition of highly undersampled projection data. (author)
International Nuclear Information System (INIS)
Wang, Li; Gac, Nicolas; Mohammad-Djafari, Ali
2015-01-01
In order to improve quality of 3D X-ray tomography reconstruction for Non Destructive Testing (NDT), we investigate in this paper hierarchical Bayesian methods. In NDT, useful prior information on the volume like the limited number of materials or the presence of homogeneous area can be included in the iterative reconstruction algorithms. In hierarchical Bayesian methods, not only the volume is estimated thanks to the prior model of the volume but also the hyper parameters of this prior. This additional complexity in the reconstruction methods when applied to large volumes (from 512 3 to 8192 3 voxels) results in an increasing computational cost. To reduce it, the hierarchical Bayesian methods investigated in this paper lead to an algorithm acceleration by Variational Bayesian Approximation (VBA) [1] and hardware acceleration thanks to projection and back-projection operators paralleled on many core processors like GPU [2]. In this paper, we will consider a Student-t prior on the gradient of the image implemented in a hierarchical way [3, 4, 1]. Operators H (forward or projection) and H t (adjoint or back-projection) implanted in multi-GPU [2] have been used in this study. Different methods will be evalued on synthetic volume 'Shepp and Logan' in terms of quality and time of reconstruction. We used several simple regularizations of order 1 and order 2. Other prior models also exists [5]. Sometimes for a discrete image, we can do the segmentation and reconstruction at the same time, then the reconstruction can be done with less projections
Bayesian image reconstruction for emission tomography based on median root prior
International Nuclear Information System (INIS)
Alenius, S.
1997-01-01
The aim of the present study was to investigate a new type of Bayesian one-step late reconstruction method which utilizes a median root prior (MRP). The method favours images which have locally monotonous radioactivity concentrations. The new reconstruction algorithm was applied to ideal simulated data, phantom data and some patient examinations with PET. The same projection data were reconstructed with filtered back-projection (FBP) and maximum likelihood-expectation maximization (ML-EM) methods for comparison. The MRP method provided good-quality images with a similar resolution to the FBP method with a ramp filter, and at the same time the noise properties were as good as with Hann-filtered FBP images. The typical artefacts seen in FBP reconstructed images outside of the object were completely removed, as was the grainy noise inside the object. Quantitativley, the resulting average regional radioactivity concentrations in a large region of interest in images produced by the MRP method corresponded to the FBP and ML-EM results but at the pixel by pixel level the MRP method proved to be the most accurate of the tested methods. In contrast to other iterative reconstruction methods, e.g. ML-EM, the MRP method was not sensitive to the number of iterations nor to the adjustment of reconstruction parameters. Only the Bayesian parameter β had to be set. The proposed MRP method is much more simple to calculate than the methods described previously, both with regard to the parameter settings and in terms of general use. The new MRP reconstruction method was shown to produce high-quality quantitative emission images with only one parameter setting in addition to the number of iterations. (orig.)
Automated comparison of Bayesian reconstructions of experimental profiles with physical models
International Nuclear Information System (INIS)
Irishkin, Maxim
2014-01-01
In this work we developed an expert system that carries out in an integrated and fully automated way i) a reconstruction of plasma profiles from the measurements, using Bayesian analysis ii) a prediction of the reconstructed quantities, according to some models and iii) an intelligent comparison of the first two steps. This system includes systematic checking of the internal consistency of the reconstructed quantities, enables automated model validation and, if a well-validated model is used, can be applied to help detecting interesting new physics in an experiment. The work shows three applications of this quite general system. The expert system can successfully detect failures in the automated plasma reconstruction and provide (on successful reconstruction cases) statistics of agreement of the models with the experimental data, i.e. information on the model validity. (author) [fr
Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images
International Nuclear Information System (INIS)
Mumcuglu, E.U.; Leahy, R.; Zhou, Z.; Cherry, S.R.
1994-01-01
The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15--25 iterations. Reconstructions are presented of an 18 FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors
Bayesian Multi-Energy Computed Tomography reconstruction approaches based on decomposition models
International Nuclear Information System (INIS)
Cai, Caifang
2013-01-01
Multi-Energy Computed Tomography (MECT) makes it possible to get multiple fractions of basis materials without segmentation. In medical application, one is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical MECT measurements are usually obtained with polychromatic X-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam poly-chromaticity fail to estimate the correct decomposition fractions and result in Beam-Hardening Artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log pre-processing and the water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on non-linear forward models counting the beam poly-chromaticity show great potential for giving accurate fraction images.This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint Maximum A Posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a non-quadratic cost function. To solve it, the use of a monotone Conjugate Gradient (CG) algorithm with suboptimal descent steps is proposed.The performances of the proposed approach are analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also
Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum
Weitzel, Nils; Hense, Andreas; Ohlwein, Christian
2017-04-01
Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were
Cophylogeny reconstruction via an approximate Bayesian computation.
Baudet, C; Donati, B; Sinaimeri, B; Crescenzi, P; Gautier, C; Matias, C; Sagot, M-F
2015-05-01
Despite an increasingly vast literature on cophylogenetic reconstructions for studying host-parasite associations, understanding the common evolutionary history of such systems remains a problem that is far from being solved. Most algorithms for host-parasite reconciliation use an event-based model, where the events include in general (a subset of) cospeciation, duplication, loss, and host switch. All known parsimonious event-based methods then assign a cost to each type of event in order to find a reconstruction of minimum cost. The main problem with this approach is that the cost of the events strongly influences the reconciliation obtained. Some earlier approaches attempt to avoid this problem by finding a Pareto set of solutions and hence by considering event costs under some minimization constraints. To deal with this problem, we developed an algorithm, called Coala, for estimating the frequency of the events based on an approximate Bayesian computation approach. The benefits of this method are 2-fold: (i) it provides more confidence in the set of costs to be used in a reconciliation, and (ii) it allows estimation of the frequency of the events in cases where the data set consists of trees with a large number of taxa. We evaluate our method on simulated and on biological data sets. We show that in both cases, for the same pair of host and parasite trees, different sets of frequencies for the events lead to equally probable solutions. Moreover, often these solutions differ greatly in terms of the number of inferred events. It appears crucial to take this into account before attempting any further biological interpretation of such reconciliations. More generally, we also show that the set of frequencies can vary widely depending on the input host and parasite trees. Indiscriminately applying a standard vector of costs may thus not be a good strategy. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
Applying Bayesian neural networks to event reconstruction in reactor neutrino experiments
International Nuclear Information System (INIS)
Xu Ye; Xu Weiwei; Meng Yixiong; Zhu Kaien; Xu Wei
2008-01-01
A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The electron samples from the Monte-Carlo simulation of the toy detector have been reconstructed by the method of Bayesian neural networks (BNNs) and the standard algorithm, a maximum likelihood method (MLD), respectively. The result of the event reconstruction using BNN has been compared with the one using MLD. Compared to MLD, the uncertainties of the electron vertex are not improved, but the energy resolutions are significantly improved using BNN. And the improvement is more obvious for the high energy electrons than the low energy ones
Pedersen, Niklas; Holyoak, David T; Newton, Angela E
2007-06-01
The Bryaceae are a large cosmopolitan moss family including genera of significant morphological and taxonomic complexity. Phylogenetic relationships within the Bryaceae were reconstructed based on DNA sequence data from all three genomic compartments. In addition, maximum parsimony and Bayesian inference were employed to reconstruct ancestral character states of 38 morphological plus four habitat characters and eight insertion/deletion events. The recovered phylogenetic patterns are generally in accord with previous phylogenies based on chloroplast DNA sequence data and three major clades are identified. The first clade comprises Bryum bornholmense, B. rubens, B. caespiticium, and Plagiobryum. This corroborates the hypothesis suggested by previous studies that several Bryum species are more closely related to Plagiobryum than to the core Bryum species. The second clade includes Acidodontium, Anomobryum, and Haplodontium, while the third clade contains the core Bryum species plus Imbribryum. Within the latter clade, B. subapiculatum and B. tenuisetum form the sister clade to Imbribryum. Reconstructions of ancestral character states under maximum parsimony and Bayesian inference suggest fourteen morphological synapomorphies for the ingroup and synapomorphies are detected for most clades within the ingroup. Maximum parsimony and Bayesian reconstructions of ancestral character states are mostly congruent although Bayesian inference shows that the posterior probability of ancestral character states may decrease dramatically when node support is taken into account. Bayesian inference also indicates that reconstructions may be ambiguous at internal nodes for highly polymorphic characters.
Bayesian tomographic reconstruction of microsystems
International Nuclear Information System (INIS)
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-01-01
The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast).To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique.In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations
Bayesian model selection of template forward models for EEG source reconstruction.
Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan
2014-06-01
Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction. Copyright © 2014 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
J. P. Werner
2015-03-01
Full Text Available Reconstructions of the late-Holocene climate rely heavily upon proxies that are assumed to be accurately dated by layer counting, such as measurements of tree rings, ice cores, and varved lake sediments. Considerable advances could be achieved if time-uncertain proxies were able to be included within these multiproxy reconstructions, and if time uncertainties were recognized and correctly modeled for proxies commonly treated as free of age model errors. Current approaches for accounting for time uncertainty are generally limited to repeating the reconstruction using each one of an ensemble of age models, thereby inflating the final estimated uncertainty – in effect, each possible age model is given equal weighting. Uncertainties can be reduced by exploiting the inferred space–time covariance structure of the climate to re-weight the possible age models. Here, we demonstrate how Bayesian hierarchical climate reconstruction models can be augmented to account for time-uncertain proxies. Critically, although a priori all age models are given equal probability of being correct, the probabilities associated with the age models are formally updated within the Bayesian framework, thereby reducing uncertainties. Numerical experiments show that updating the age model probabilities decreases uncertainty in the resulting reconstructions, as compared with the current de facto standard of sampling over all age models, provided there is sufficient information from other data sources in the spatial region of the time-uncertain proxy. This approach can readily be generalized to non-layer-counted proxies, such as those derived from marine sediments.
International Nuclear Information System (INIS)
Irishkin, M.; Imbeaux, F.; Aniel, T.; Artaud, J.F.
2015-01-01
Highlights: • We developed a method for automated comparison of experimental data with models. • A unique platform implements Bayesian analysis and integrated modelling tools. • The method is tokamak-generic and is applied to Tore Supra and JET pulses. • Validation of a heat transport model is carried out. • We quantified the uncertainties due to Te profiles in current diffusion simulations. - Abstract: In the context of present and future long pulse tokamak experiments yielding a growing size of measured data per pulse, automating data consistency analysis and comparisons of measurements with models is a critical matter. To address these issues, the present work describes an expert system that carries out in an integrated and fully automated way (i) a reconstruction of plasma profiles from the measurements, using Bayesian analysis (ii) a prediction of the reconstructed quantities, according to some models and (iii) a comparison of the first two steps. The first application shown is devoted to the development of an automated comparison method between the experimental plasma profiles reconstructed using Bayesian methods and time dependent solutions of the transport equations. The method was applied to model validation of a simple heat transport model with three radial shape options. It has been tested on a database of 21 Tore Supra and 14 JET shots. The second application aims at quantifying uncertainties due to the electron temperature profile in current diffusion simulations. A systematic reconstruction of the Ne, Te, Ti profiles was first carried out for all time slices of the pulse. The Bayesian 95% highest probability intervals on the Te profile reconstruction were then used for (i) data consistency check of the flux consumption and (ii) defining a confidence interval for the current profile simulation. The method has been applied to one Tore Supra pulse and one JET pulse.
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J.
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems.
Merkatas, Christos; Kaloudis, Konstantinos; Hatjispyros, Spyridon J
2017-06-01
We propose a Bayesian nonparametric mixture model for the reconstruction and prediction from observed time series data, of discretized stochastic dynamical systems, based on Markov Chain Monte Carlo methods. Our results can be used by researchers in physical modeling interested in a fast and accurate estimation of low dimensional stochastic models when the size of the observed time series is small and the noise process (perhaps) is non-Gaussian. The inference procedure is demonstrated specifically in the case of polynomial maps of an arbitrary degree and when a Geometric Stick Breaking mixture process prior over the space of densities, is applied to the additive errors. Our method is parsimonious compared to Bayesian nonparametric techniques based on Dirichlet process mixtures, flexible and general. Simulations based on synthetic time series are presented.
Accurate reconstruction of insertion-deletion histories by statistical phylogenetics.
Directory of Open Access Journals (Sweden)
Oscar Westesson
Full Text Available The Multiple Sequence Alignment (MSA is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history, it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.
Reconstructing Fire History: An Exercise in Dendrochronology
Lafon, Charles W.
2005-01-01
Dendrochronology is used widely to reconstruct the history of forest disturbances. I created an exercise that introduces the use of dendrochronology to investigate fire history and forest dynamics. The exercise also demonstrates how the dendrochronological technique of crossdating is employed to age dead trees and identify missing rings. I…
Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM☆
López, J.D.; Litvak, V.; Espinosa, J.J.; Friston, K.; Barnes, G.R.
2014-01-01
The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. PMID:24041874
Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.
López, J D; Litvak, V; Espinosa, J J; Friston, K; Barnes, G R
2014-01-01
The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.
Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.
Deshwar, Amit G; Vembu, Shankar; Morris, Quaid
2015-01-01
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…
Bayesian phylogeography finds its roots.
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Philippe Lemey
2009-09-01
Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
International Nuclear Information System (INIS)
Cai, C.; Rodet, T.; Mohammad-Djafari, A.; Legoupil, S.
2013-01-01
Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also
Directory of Open Access Journals (Sweden)
Qing Li
2018-04-01
Full Text Available High-speed remote transmission and large-capacity data storage are difficult issues in signals acquisition of rotating machines condition monitoring. To address these concerns, a novel multichannel signals reconstruction approach based on tunable Q-factor wavelet transform-morphological component analysis (TQWT-MCA and sparse Bayesian iteration algorithm combined with step-impulse dictionary is proposed under the frame of compressed sensing (CS. To begin with, to prevent the periodical impulses loss and effectively separate periodical impulses from the external noise and additive interference components, the TQWT-MCA method is introduced to divide the raw vibration signal into low-resonance component (LRC, i.e., periodical impulses and high-resonance component (HRC, thus, the periodical impulses are preserved effectively. Then, according to the amplitude range of generated LRC, the step-impulse dictionary atom is designed to match the physical structure of periodical impulses. Furthermore, the periodical impulses and HRC are reconstructed by the sparse Bayesian iteration combined with step-impulse dictionary, respectively, finally, the final reconstructed raw signals are obtained by adding the LRC and HRC, meanwhile, the fidelity of the final reconstructed signals is tested by the envelop spectrum and error analysis, respectively. In this work, the proposed algorithm is applied to simulated signal and engineering multichannel signals of a gearbox with multiple faults. Experimental results demonstrate that the proposed approach significantly improves the reconstructive accuracy compared with the state-of-the-art methods such as non-convex Lq (q = 0.5 regularization, spatiotemporal sparse Bayesian learning (SSBL and L1-norm, etc. Additionally, the processing time, i.e., speed of storage and transmission has increased dramatically, more importantly, the fault characteristics of the gearbox with multiple faults are detected and saved, i.e., the
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir; Al-Naffouri, Tareq Y.
2013-01-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics
Bayesian analysis in plant pathology.
Mila, A L; Carriquiry, A L
2004-09-01
ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.
Woock, P.; Pak, Alexey
2014-01-01
To explore the seafloor, a side-scan sonar emits a directed acoustic signal and then records the returning (reflected) signal intensity as a function of time. The inversion of that process is not unique: multiple shapes may lead to identical measured responses. In this work, we suggest a Bayesian approach to reconstructing the 3D shape of the seafloor from multiple sonar measurements, inspired by the state-of-the-art methods of inverse raytracing that originated in computer vision. The space ...
Bayesian image reconstruction in SPECT using higher order mechanical models as priors
International Nuclear Information System (INIS)
Lee, S.J.; Gindi, G.; Rangarajan, A.
1995-01-01
While the ML-EM (maximum-likelihood-expectation maximization) algorithm for reconstruction for emission tomography is unstable due to the ill-posed nature of the problem, Bayesian reconstruction methods overcome this instability by introducing prior information, often in the form of a spatial smoothness regularizer. More elaborate forms of smoothness constraints may be used to extend the role of the prior beyond that of a stabilizer in order to capture actual spatial information about the object. Previously proposed forms of such prior distributions were based on the assumption of a piecewise constant source distribution. Here, the authors propose an extension to a piecewise linear model--the weak plate--which is more expressive than the piecewise constant model. The weak plate prior not only preserves edges but also allows for piecewise ramplike regions in the reconstruction. Indeed, for the application in SPECT, such ramplike regions are observed in ground-truth source distributions in the form of primate autoradiographs of rCBF radionuclides. To incorporate the weak plate prior in a MAP approach, the authors model the prior as a Gibbs distribution and use a GEM formulation for the optimization. They compare quantitative performance of the ML-EM algorithm, a GEM algorithm with a prior favoring piecewise constant regions, and a GEM algorithm with the weak plate prior. Pointwise and regional bias and variance of ensemble image reconstructions are used as indications of image quality. The results show that the weak plate and membrane priors exhibit improved bias and variance relative to ML-EM techniques
Bayesian Models for Streamflow and River Network Reconstruction using Tree Rings
Ravindranath, A.; Devineni, N.
2016-12-01
Water systems face non-stationary, dynamically shifting risks due to shifting societal conditions and systematic long-term variations in climate manifesting as quasi-periodic behavior on multi-decadal time scales. Water systems are thus vulnerable to long periods of wet or dry hydroclimatic conditions. Streamflow is a major component of water systems and a primary means by which water is transported to serve ecosystems' and human needs. Thus, our concern is in understanding streamflow variability. Climate variability and impacts on water resources are crucial factors affecting streamflow, and multi-scale variability increases risk to water sustainability and systems. Dam operations are necessary for collecting water brought by streamflow while maintaining downstream ecological health. Rules governing dam operations are based on streamflow records that are woefully short compared to periods of systematic variation present in the climatic factors driving streamflow variability and non-stationarity. We use hierarchical Bayesian regression methods in order to reconstruct paleo-streamflow records for dams within a basin using paleoclimate proxies (e.g. tree rings) to guide the reconstructions. The riverine flow network for the entire basin is subsequently modeled hierarchically using feeder stream and tributary flows. This is a starting point in analyzing streamflow variability and risks to water systems, and developing a scientifically-informed dynamic risk management framework for formulating dam operations and water policies to best hedge such risks. We will apply this work to the Missouri and Delaware River Basins (DRB). Preliminary results of streamflow reconstructions for eight dams in the upper DRB using standard Gaussian regression with regional tree ring chronologies give streamflow records that now span two to two and a half centuries, and modestly smoothed versions of these reconstructed flows indicate physically-justifiable trends in the time series.
Crossing statistic: reconstructing the expansion history of the universe
International Nuclear Information System (INIS)
Shafieloo, Arman
2012-01-01
We present that by combining Crossing Statistic [1,2] and Smoothing method [3-5] one can reconstruct the expansion history of the universe with a very high precision without considering any prior on the cosmological quantities such as the equation of state of dark energy. We show that the presented method performs very well in reconstruction of the expansion history of the universe independent of the underlying models and it works well even for non-trivial dark energy models with fast or slow changes in the equation of state of dark energy. Accuracy of the reconstructed quantities along with independence of the method to any prior or assumption gives the proposed method advantages to the other non-parametric methods proposed before in the literature. Applying on the Union 2.1 supernovae combined with WiggleZ BAO data we present the reconstructed results and test the consistency of the two data sets in a model independent manner. Results show that latest available supernovae and BAO data are in good agreement with each other and spatially flat ΛCDM model is in concordance with the current data
Bayesian image restoration for medical images using radon transform
International Nuclear Information System (INIS)
Shouno, Hayaru; Okada, Masato
2010-01-01
We propose an image reconstruction algorithm using Bayesian inference for Radon transformed observation data, which often appears in the field of medical image reconstruction known as computed tomography (CT). In order to apply our Bayesian reconstruction method, we introduced several hyper-parameters that control the ratio between prior information and the fidelity of the observation process. Since the quality of the reconstructed image is influenced by the estimation accuracy of these hyper-parameters, we propose an inference method for them based on the marginal likelihood maximization principle as well as the image reconstruction method. We are able to demonstrate a reconstruction result superior to that obtained using the conventional filtered back projection method. (author)
Diffusion archeology for diffusion progression history reconstruction.
Sefer, Emre; Kingsford, Carl
2016-11-01
Diffusion through graphs can be used to model many real-world processes, such as the spread of diseases, social network memes, computer viruses, or water contaminants. Often, a real-world diffusion cannot be directly observed while it is occurring - perhaps it is not noticed until some time has passed, continuous monitoring is too costly, or privacy concerns limit data access. This leads to the need to reconstruct how the present state of the diffusion came to be from partial diffusion data. Here, we tackle the problem of reconstructing a diffusion history from one or more snapshots of the diffusion state. This ability can be invaluable to learn when certain computer nodes are infected or which people are the initial disease spreaders to control future diffusions. We formulate this problem over discrete-time SEIRS-type diffusion models in terms of maximum likelihood. We design methods that are based on submodularity and a novel prize-collecting dominating-set vertex cover (PCDSVC) relaxation that can identify likely diffusion steps with some provable performance guarantees. Our methods are the first to be able to reconstruct complete diffusion histories accurately in real and simulated situations. As a special case, they can also identify the initial spreaders better than the existing methods for that problem. Our results for both meme and contaminant diffusion show that the partial diffusion data problem can be overcome with proper modeling and methods, and that hidden temporal characteristics of diffusion can be predicted from limited data.
O'Reilly, Joseph E; Puttick, Mark N; Parry, Luke; Tanner, Alastair R; Tarver, James E; Fleming, James; Pisani, Davide; Donoghue, Philip C J
2016-04-01
Different analytical methods can yield competing interpretations of evolutionary history and, currently, there is no definitive method for phylogenetic reconstruction using morphological data. Parsimony has been the primary method for analysing morphological data, but there has been a resurgence of interest in the likelihood-based Mk-model. Here, we test the performance of the Bayesian implementation of the Mk-model relative to both equal and implied-weight implementations of parsimony. Using simulated morphological data, we demonstrate that the Mk-model outperforms equal-weights parsimony in terms of topological accuracy, and implied-weights performs the most poorly. However, the Mk-model produces phylogenies that have less resolution than parsimony methods. This difference in the accuracy and precision of parsimony and Bayesian approaches to topology estimation needs to be considered when selecting a method for phylogeny reconstruction. © 2016 The Authors.
Equivalent thermal history reconstruction from a partially crystallized glass-ceramic sensor array
Heeg, Bauke
2015-11-01
The basic concept of a thermal history sensor is that it records the accumulated exposure to some unknown, typically varying temperature profile for a certain amount of time. Such a sensor is considered to be capable of measuring the duration of several (N) temperature intervals. For this purpose, the sensor deploys multiple (M) sensing elements, each with different temperature sensitivity. At the end of some thermal exposure for a known period of time, the sensor array is read-out and an estimate is made of the set of N durations of the different temperature ranges. A potential implementation of such a sensor was pioneered by Fair et al. [Sens. Actuators, A 141, 245 (2008)], based on glass-ceramic materials with different temperature-dependent crystallization dynamics. In their work, it was demonstrated that an array of sensor elements can be made sensitive to slight differences in temperature history. Further, a forward crystallization model was used to simulate the variations in sensor array response to differences in the temperature history. The current paper focusses on the inverse aspect of temperature history reconstruction from a hypothetical sensor array output. The goal of such a reconstruction is to find an equivalent thermal history that is the closest representation of the true thermal history, i.e., the durations of a set of temperature intervals that result in a set of fractional crystallization values which is closest to the one resulting from the true thermal history. One particular useful simplification in both the sensor model as well as in its practical implementation is the omission of nucleation effects. In that case, least squares models can be used to approximate the sensor response and make reconstruction estimates. Even with this simplification, sensor noise can have a destabilizing effect on possible reconstruction solutions, which is evaluated using simulations. Both regularization and non-negativity constrained least squares
Frey, Jordan D; Alperovich, Michael; Levine, Jamie P; Choi, Mihye; Karp, Nolan S
2017-07-01
History of smoking has been implicated as a risk factor for reconstructive complications in nipple-sparing mastectomy (NSM), however there have been no direct analyses of outcomes in smokers and nonsmokers. All patients undergoing NSM at New York University Langone Medical Center from 2006 to 2014 were identified. Outcomes were compared for those with and without a smoking history and stratified by pack-year smoking history and years-to-quitting (YTQ). A total of 543 nipple-sparing mastectomies were performed from 2006 to 2014 with a total of 49 in patients with a history of smoking. Reconstructive outcomes in NSM between those with and without a smoking history were equivalent. Those with a smoking history were not significantly more likely to have mastectomy flap necrosis (p = 0.6251), partial (p = 0.8564), or complete (p = 0.3365) nipple-areola complex (NAC) necrosis. Likewise, active smokers alone did not have a higher risk of complications compared to nonsmokers or those with smoking history. Comparing nonsmokers and those with a less or greater than 10 pack-year smoking history, those with a > 10 pack-year history had significantly more complete NAC necrosis (p = 0.0114, smoking history or >5 YTQ prior to NSM were equivalent to those without a smoking history. We demonstrate that NSM may be safely offered to those with a smoking history although a > 10 pack-year smoking history or <5 YTQ prior to NSM may impart a higher risk of reconstructive complications, including complete NAC necrosis. © 2017 Wiley Periodicals, Inc.
Efficient reconstruction of contaminant release history
Energy Technology Data Exchange (ETDEWEB)
Alezander, Francis [Los Alamos National Laboratory; Anghel, Marian [Los Alamos National Laboratory; Gulbahce, Natali [NON LANL; Tartakovsky, Daniel [NON LANL
2009-01-01
We present a generalized hybrid Monte Carlo (GHMC) method for fast, statistically optimal reconstruction of release histories of reactive contaminants. The approach is applicable to large-scale, strongly nonlinear systems with parametric uncertainties and data corrupted by measurement errors. The use of discrete adjoint equations facilitates numerical implementation of GHMC, without putting any restrictions on the degree of nonlinearity of advection-dispersion-reaction equations that are used to described contaminant transport in the subsurface. To demonstrate the salient features of the proposed algorithm, we identify the spatial extent of a distributed source of contamination from concentration measurements of a reactive solute.
International Nuclear Information System (INIS)
Foudray, Angela M K; Levin, Craig S
2007-01-01
PET at the highest level is an inverse problem: reconstruct the location of the emission (which localize biological function) from detected photons. Ideally, one would like to directly measure an annihilation photon's incident direction on the detector. In the developed algorithm, Bayesian Estimation for Angle Recovery (BEAR), we utilized the increased information gathered from localizing photon interactions in the detector and developed a Bayesian estimator for a photon's incident direction. Probability distribution functions (PDFs) were filled using an interaction energy weighted mean or center of mass (COM) reference space, which had the following computational advantages: (1) a significant reduction in the size of the data in measurement space, making further manipulation and searches faster (2) the construction of COM space does not depend on measurement location, it takes advantage of measurement symmetries, and data can be added to the training set without knowledge and recalculation of prior training data, (3) calculation of posterior probability map is fully parallelizable, it can scale to any number of processors. These PDFs were used to estimate the point spread function (PSF) in incident angle space for (i) algorithm assessment and (ii) to provide probability selection criteria for classification. The algorithm calculates both the incident θ and φ angle, with ∼16 degrees RMS in both angles, limiting the incoming direction to a narrow cone. Feature size did not improve using the BEAR algorithm as an angle filter, but the contrast ratio improved 40% on average
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir
2013-11-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
Awakening the BALROG: BAyesian Location Reconstruction Of GRBs
Burgess, J. Michael; Yu, Hoi-Fung; Greiner, Jochen; Mortlock, Daniel J.
2018-05-01
The accurate spatial location of gamma-ray bursts (GRBs) is crucial for both accurately characterizing their spectra and follow-up observations by other instruments. The Fermi Gamma-ray Burst Monitor (GBM) has the largest field of view for detecting GRBs as it views the entire unocculted sky, but as a non-imaging instrument it relies on the relative count rates observed in each of its 14 detectors to localize transients. Improving its ability to accurately locate GRBs and other transients is vital to the paradigm of multimessenger astronomy, including the electromagnetic follow-up of gravitational wave signals. Here we present the BAyesian Location Reconstruction Of GRBs (BALROG) method for localizing and characterizing GBM transients. Our approach eliminates the systematics of previous approaches by simultaneously fitting for the location and spectrum of a source. It also correctly incorporates the uncertainties in the location of a transient into the spectral parameters and produces reliable positional uncertainties for both well-localized sources and those for which the GBM data cannot effectively constrain the position. While computationally expensive, BALROG can be implemented to enable quick follow-up of all GBM transient signals. Also, we identify possible response problems that require attention and caution when using standard, public GBM detector response matrices. Finally, we examine the effects of including the uncertainty in location on the spectral parameters of GRB 080916C. We find that spectral parameters change and no extra components are required when these effects are included in contrast to when we use a fixed location. This finding has the potential to alter both the GRB spectral catalogues and the reported spectral composition of some well-known GRBs.
The Development of Bayesian Theory and Its Applications in Business and Bioinformatics
Zhang, Yifei
2018-03-01
Bayesian Theory originated from an Essay of a British mathematician named Thomas Bayes in 1763, and after its development in 20th century, Bayesian Statistics has been taking a significant part in statistical study of all fields. Due to the recent breakthrough of high-dimensional integral, Bayesian Statistics has been improved and perfected, and now it can be used to solve problems that Classical Statistics failed to solve. This paper summarizes Bayesian Statistics’ history, concepts and applications, which are illustrated in five parts: the history of Bayesian Statistics, the weakness of Classical Statistics, Bayesian Theory and its development and applications. The first two parts make a comparison between Bayesian Statistics and Classical Statistics in a macroscopic aspect. And the last three parts focus on Bayesian Theory in specific -- from introducing some particular Bayesian Statistics’ concepts to listing their development and finally their applications.
Prior approval: the growth of Bayesian methods in psychology.
Andrews, Mark; Baguley, Thom
2013-02-01
Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.
A parametric reconstruction of the deceleration parameter
Energy Technology Data Exchange (ETDEWEB)
Al Mamon, Abdulla [Manipal University, Manipal Centre for Natural Sciences, Manipal (India); Visva-Bharati, Department of Physics, Santiniketan (India); Das, Sudipta [Visva-Bharati, Department of Physics, Santiniketan (India)
2017-07-15
The present work is based on a parametric reconstruction of the deceleration parameter q(z) in a model for the spatially flat FRW universe filled with dark energy and non-relativistic matter. In cosmology, the parametric reconstruction technique deals with an attempt to build up a model by choosing some specific evolution scenario for a cosmological parameter and then estimate the values of the parameters with the help of different observational datasets. In this paper, we have proposed a logarithmic parametrization of q(z) to probe the evolution history of the universe. Using the type Ia supernova, baryon acoustic oscillation and the cosmic microwave background datasets, the constraints on the arbitrary model parameters q{sub 0} and q{sub 1} are obtained (within 1σ and 2σ confidence limits) by χ{sup 2}-minimization technique. We have then reconstructed the deceleration parameter, the total EoS parameter ω{sub tot}, the jerk parameter and have compared the reconstructed results of q(z) with other well-known parametrizations of q(z). We have also shown that two model selection criteria (namely, the Akaike information criterion and Bayesian information criterion) provide a clear indication that our reconstructed model is well consistent with other popular models. (orig.)
Courtejoie, Noémie; Salje, Henrik; Durand, Benoît; Zanella, Gina; Cauchemez, Simon
2018-05-17
Bluetongue virus is a vector-borne pathogen affecting ruminants that has caused major epidemics in France. Reconstructing the history of bluetongue in French cattle under control strategies such as vaccination has been hampered by the high level of sub-clinical infection, incomplete case data and poor understanding of vaccine uptake over time and space. To tackle these challenges, we used three age-structured serological surveys carried out in cattle (N = 22,342) from ten administrative subdivisions called departments. We fitted catalytic models within a Bayesian MCMC framework to reconstruct the force of seroconversion from infection or vaccination, and the population-level susceptibility per semester between 2007 and 2016. In the departments of the study area, we estimated that 36% of cattle had been infected prior to vaccine rollout that became compulsory from July 2008. The last outbreak case was notified in December 2009, at which time 83% of the animals were seropositive, under the cumulative effect of vaccination and infection. The probability of seroconversion per semester dropped below 10% after 2010 when vaccination became optional. Vaccine uptake was smaller during the 2012 campaign than during the one in 2011, with strong regional contrasts. Eighty four percent of cattle were susceptible when bluetongue re-emerged in 2015. Thus, serological surveys can be used to estimate vaccine uptake and the magnitude of infection, the relative effect of which can sometimes be inferred using prior knowledge on reported incidence and vaccination dates. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
A STUDY ON DYNAMIC LOAD HISTORY RECONSTRUCTION USING PSEUDO-INVERSE METHODS
Santos, Ariane Rebelato Silva dos; Marczak, Rogério José
2017-01-01
Considering that the vibratory forces generally cannot be measured directly at the interface of two bodies, an inverse method is studied in the present work to recover the load history in such cases. The proposed technique attempts to reconstruct the dynamic loads history by using a frequency domain analysis and Moore-Penrose pseudo-inverses of the frequency response function (FRF) of the system. The methodology consists in applying discrete dynamic loads on a finite element model in the time...
A phylogenetic Kalman filter for ancestral trait reconstruction using molecular data.
Lartillot, Nicolas
2014-02-15
Correlation between life history or ecological traits and genomic features such as nucleotide or amino acid composition can be used for reconstructing the evolutionary history of the traits of interest along phylogenies. Thus far, however, such ancestral reconstructions have been done using simple linear regression approaches that do not account for phylogenetic inertia. These reconstructions could instead be seen as a genuine comparative regression problem, such as formalized by classical generalized least-square comparative methods, in which the trait of interest and the molecular predictor are represented as correlated Brownian characters coevolving along the phylogeny. Here, a Bayesian sampler is introduced, representing an alternative and more efficient algorithmic solution to this comparative regression problem, compared with currently existing generalized least-square approaches. Technically, ancestral trait reconstruction based on a molecular predictor is shown to be formally equivalent to a phylogenetic Kalman filter problem, for which backward and forward recursions are developed and implemented in the context of a Markov chain Monte Carlo sampler. The comparative regression method results in more accurate reconstructions and a more faithful representation of uncertainty, compared with simple linear regression. Application to the reconstruction of the evolution of optimal growth temperature in Archaea, using GC composition in ribosomal RNA stems and amino acid composition of a sample of protein-coding genes, confirms previous findings, in particular, pointing to a hyperthermophilic ancestor for the kingdom. The program is freely available at www.phylobayes.org.
Parallelized Bayesian inversion for three-dimensional dental X-ray imaging.
Kolehmainen, Ville; Vanne, Antti; Siltanen, Samuli; Järvenpää, Seppo; Kaipio, Jari P; Lassas, Matti; Kalke, Martti
2006-02-01
Diagnostic and operational tasks based on dental radiology often require three-dimensional (3-D) information that is not available in a single X-ray projection image. Comprehensive 3-D information about tissues can be obtained by computerized tomography (CT) imaging. However, in dental imaging a conventional CT scan may not be available or practical because of high radiation dose, low-resolution or the cost of the CT scanner equipment. In this paper, we consider a novel type of 3-D imaging modality for dental radiology. We consider situations in which projection images of the teeth are taken from a few sparsely distributed projection directions using the dentist's regular (digital) X-ray equipment and the 3-D X-ray attenuation function is reconstructed. A complication in these experiments is that the reconstruction of the 3-D structure based on a few projection images becomes an ill-posed inverse problem. Bayesian inversion is a well suited framework for reconstruction from such incomplete data. In Bayesian inversion, the ill-posed reconstruction problem is formulated in a well-posed probabilistic form in which a priori information is used to compensate for the incomplete information of the projection data. In this paper we propose a Bayesian method for 3-D reconstruction in dental radiology. The method is partially based on Kolehmainen et al. 2003. The prior model for dental structures consist of a weighted l1 and total variation (TV)-prior together with the positivity prior. The inverse problem is stated as finding the maximum a posteriori (MAP) estimate. To make the 3-D reconstruction computationally feasible, a parallelized version of an optimization algorithm is implemented for a Beowulf cluster computer. The method is tested with projection data from dental specimens and patient data. Tomosynthetic reconstructions are given as reference for the proposed method.
Genome rearrangements and phylogeny reconstruction in Yersinia pestis.
Bochkareva, Olga O; Dranenko, Natalia O; Ocheredko, Elena S; Kanevsky, German M; Lozinsky, Yaroslav N; Khalaycheva, Vera A; Artamonova, Irena I; Gelfand, Mikhail S
2018-01-01
Genome rearrangements have played an important role in the evolution of Yersinia pestis from its progenitor Yersinia pseudotuberculosis . Traditional phylogenetic trees for Y. pestis based on sequence comparison have short internal branches and low bootstrap supports as only a small number of nucleotide substitutions have occurred. On the other hand, even a small number of genome rearrangements may resolve topological ambiguities in a phylogenetic tree. We reconstructed phylogenetic trees based on genome rearrangements using several popular approaches such as Maximum likelihood for Gene Order and the Bayesian model of genome rearrangements by inversions. We also reconciled phylogenetic trees for each of the three CRISPR loci to obtain an integrated scenario of the CRISPR cassette evolution. Analysis of contradictions between the obtained evolutionary trees yielded numerous parallel inversions and gain/loss events. Our data indicate that an integrated analysis of sequence-based and inversion-based trees enhances the resolution of phylogenetic reconstruction. In contrast, reconstructions of strain relationships based on solely CRISPR loci may not be reliable, as the history is obscured by large deletions, obliterating the order of spacer gains. Similarly, numerous parallel gene losses preclude reconstruction of phylogeny based on gene content.
DEFF Research Database (Denmark)
Alberdi, Antton; Gilbert, M. Thomas P; Razgour, Orly
2015-01-01
Aim: We used an integrative approach to reconstruct the evolutionary history of the alpine long-eared bat, Plecotus macrobullaris, to test whether the variable effects of Pleistocene climatic oscillations across geographical regions led to contrasting population-level demographic histories within...... a single species. Location: The Western Palaearctic. Methods: We sequenced the complete mitochondrial genomes of 57 individuals from across the distribution of the species. The analysis integrated ecological niche modelling (ENM), approximate Bayesian computation (ABC), measures of genetic diversity...... and Bayesian phylogenetic methods. Results: We identified two deep lineages: a western lineage, restricted to the Pyrenees and the Alps, and an eastern lineage, which expanded across the mountain ranges east of the Dinarides (Croatia). ENM projections of past conditions predicted that climatic suitability...
Leonhardt, Roman; Fabian, Karl
2007-01-01
The Earth's magnetic field changed its polarity from the last reversed into today's normal state approximately 780 000 years ago. While before and after this so called Matuyama/Brunhes reversal, the Earth magnetic field was essentially an axial dipole, the details of its transitional structure are still largely unknown. Here, a Bayesian inversion method is developed to reconstruct the spherical harmonic expansion of this transitional field from paleomagnetic data. This is achieved by minimizing the total variational power at the core-mantle boundary during the transition under paleomagnetic constraints. The validity of the inversion technique is proved in two ways. First by inverting synthetic data sets from a modeled reversal. Here it is possible to reliably reconstruct the Gauss coefficients even from noisy records. Second by iteratively combining four geographically distributed high quality paleomagnetic records of the Matuyama/Brunhes reversal into a single geometric reversal scenario without assuming an a priori common age model. The obtained spatio-temporal reversal scenario successfully predicts most independent Matuyama/Brunhes transitional records. Therefore, the obtained global reconstruction based on paleomagnetic data invites to compare the inferred transitional field structure with results from numerical geodynamo models regarding the morphology of the transitional field. It is found that radial magnetic flux patches form at the equator and move polewards during the transition. Our model indicates an increase of non-dipolar energy prior to the last reversal and a non-dipolar dominance during the transition. Thus, the character and information of surface geomagnetic field records is strongly site dependent. The reconstruction also offers new answers to the question of existence of preferred longitudinal bands during the transition and to the problem of reversal duration. Different types of directional variations of the surface geomagnetic field
Bayesian nonparametric dictionary learning for compressed sensing MRI.
Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping
2014-12-01
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
Bayesian reconstruction of photon interaction sequences for high-resolution PET detectors
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Pratx, Guillem; Levin, Craig S [Molecular Imaging Program at Stanford, Department of Radiology, Stanford, CA (United States)], E-mail: cslevin@stanford.edu
2009-09-07
Realizing the full potential of high-resolution positron emission tomography (PET) systems involves accurately positioning events in which the annihilation photon deposits all its energy across multiple detector elements. Reconstructing the complete sequence of interactions of each photon provides a reliable way to select the earliest interaction because it ensures that all the interactions are consistent with one another. Bayesian estimation forms a natural framework to maximize the consistency of the sequence with the measurements while taking into account the physics of {gamma}-ray transport. An inherently statistical method, it accounts for the uncertainty in the measured energy and position of each interaction. An algorithm based on maximum a posteriori (MAP) was evaluated for computer simulations. For a high-resolution PET system based on cadmium zinc telluride detectors, 93.8% of the recorded coincidences involved at least one photon multiple-interactions event (PMIE). The MAP estimate of the first interaction was accurate for 85.2% of the single photons. This represents a two-fold reduction in the number of mispositioned events compared to minimum pair distance, a simpler yet efficient positioning method. The point-spread function of the system presented lower tails and higher peak value when MAP was used. This translated into improved image quality, which we quantified by studying contrast and spatial resolution gains.
Soon, Villu; Saarma, Urmas
2011-07-01
The ignita species group within the genus Chrysis includes over 100 cuckoo wasp species, which all lead a parasitic lifestyle and exhibit very similar morphology. The lack of robust, diagnostic morphological characters has hindered phylogenetic reconstructions and contributed to frequent misidentification and inconsistent interpretations of species in this group. Therefore, molecular phylogenetic analysis is the most suitable approach for resolving the phylogeny and taxonomy of this group. We present a well-resolved phylogeny of the Chrysis ignita species group based on mitochondrial sequence data from 41 ingroup and six outgroup taxa. Although our emphasis was on European taxa, we included samples from most of the distribution range of the C. ignita species group to test for monophyly. We used a continuous mitochondrial DNA sequence consisting of 16S rRNA, tRNA(Val), 12S rRNA and ND4. The location of the ND4 gene at the 3' end of this continuous sequence, following 12S rRNA, represents a novel mitochondrial gene arrangement for insects. Due to difficulties in aligning rRNA genes, two different Bayesian approaches were employed to reconstruct phylogeny: (1) using a reduced data matrix including only those positions that could be aligned with confidence; or (2) using the full sequence dataset while estimating alignment and phylogeny simultaneously. In addition maximum-parsimony and maximum-likelihood analyses were performed to test the robustness of the Bayesian approaches. Although all approaches yielded trees with similar topology, considerably more nodes were resolved with analyses using the full data matrix. Phylogenetic analysis supported the monophyly of the C. ignita species group and divided its species into well-supported clades. The resultant phylogeny was only partly in accordance with published subgroupings based on morphology. Our results suggest that several taxa currently treated as subspecies or names treated as synonyms may in fact constitute
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Daniel Magee
2017-02-01
Full Text Available Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014-15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.
McNally, Kevin; Cotton, Richard; Cocker, John; Jones, Kate; Bartels, Mike; Rick, David; Price, Paul; Loizou, George
2012-01-01
There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure. PMID:22719759
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Kevin McNally
2012-01-01
Full Text Available There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure.
International Nuclear Information System (INIS)
Chan, M.T.; Herman, G.T.; Levitan, E.
1996-01-01
We demonstrate that (i) classical methods of image reconstruction from projections can be improved upon by considering the output of such a method as a distorted version of the original image and applying a Bayesian approach to estimate from it the original image (based on a model of distortion and on a Gibbs distribution as the prior) and (ii) by selecting an open-quotes image-modelingclose quotes prior distribution (i.e., one which is such that it is likely that a random sample from it shares important characteristics of the images of the application area) one can improve over another Gibbs prior formulated using only pairwise interactions. We illustrate our approach using simulated Positron Emission Tomography (PET) data from realistic brain phantoms. Since algorithm performance ultimately depends on the diagnostic task being performed. we examine a number of different medically relevant figures of merit to give a fair comparison. Based on a training-and-testing evaluation strategy, we demonstrate that statistically significant improvements can be obtained using the proposed approach
DEFF Research Database (Denmark)
Heller, Rasmus; Chikhi, Lounes; Siegismund, Hans
2013-01-01
Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading...... when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences...... the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure...
Progress on Bayesian Inference of the Fast Ion Distribution Function
DEFF Research Database (Denmark)
Stagner, L.; Heidbrink, W.W,; Chen, X.
2013-01-01
. However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and weight functions that describe the phase space...... sensitivity of the measurements are incorporated into Bayesian likelihood probabilities. Prior probabilities describe physical constraints. This poster will show reconstructions of classically described, low-power, MHD-quiescent distribution functions from actual FIDA measurements. A description of the full...
Robustness of ancestral sequence reconstruction to phylogenetic uncertainty.
Hanson-Smith, Victor; Kolaczkowski, Bryan; Thornton, Joseph W
2010-09-01
Ancestral sequence reconstruction (ASR) is widely used to formulate and test hypotheses about the sequences, functions, and structures of ancient genes. Ancestral sequences are usually inferred from an alignment of extant sequences using a maximum likelihood (ML) phylogenetic algorithm, which calculates the most likely ancestral sequence assuming a probabilistic model of sequence evolution and a specific phylogeny--typically the tree with the ML. The true phylogeny is seldom known with certainty, however. ML methods ignore this uncertainty, whereas Bayesian methods incorporate it by integrating the likelihood of each ancestral state over a distribution of possible trees. It is not known whether Bayesian approaches to phylogenetic uncertainty improve the accuracy of inferred ancestral sequences. Here, we use simulation-based experiments under both simplified and empirically derived conditions to compare the accuracy of ASR carried out using ML and Bayesian approaches. We show that incorporating phylogenetic uncertainty by integrating over topologies very rarely changes the inferred ancestral state and does not improve the accuracy of the reconstructed ancestral sequence. Ancestral state reconstructions are robust to uncertainty about the underlying tree because the conditions that produce phylogenetic uncertainty also make the ancestral state identical across plausible trees; conversely, the conditions under which different phylogenies yield different inferred ancestral states produce little or no ambiguity about the true phylogeny. Our results suggest that ML can produce accurate ASRs, even in the face of phylogenetic uncertainty. Using Bayesian integration to incorporate this uncertainty is neither necessary nor beneficial.
Vertex Reconstruction for AEGIS’ FACT Detector
Themistokleous, Neofytos
2017-01-01
My project dealt with the development of a vertex reconstruction technique to discriminate antihydrogen from background signals in the AEGIS apparatus. It involved the creation of a Toy Monte-Carlo to simulate particle annihilation events, and a vertex reconstruction utility based on the Bayesian theory of probability. The ﬁrst results based on 107 generated events with single track in the detector are encouraging. For such events, the algorithm can reconstruct the z-coordinate accurately , while for the r-coordinate the result is less accurate.
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Yadira Chinique de Armas
Full Text Available The general lack of well-preserved juvenile skeletal remains from Caribbean archaeological sites has, in the past, prevented evaluations of juvenile dietary changes. Canímar Abajo (Cuba, with a large number of well-preserved juvenile and adult skeletal remains, provided a unique opportunity to fully assess juvenile paleodiets from an ancient Caribbean population. Ages for the start and the end of weaning and possible food sources used for weaning were inferred by combining the results of two Bayesian probability models that help to reduce some of the uncertainties inherent to bone collagen isotope based paleodiet reconstructions. Bone collagen (31 juveniles, 18 adult females was used for carbon and nitrogen isotope analyses. The isotope results were assessed using two Bayesian probability models: Weaning Ages Reconstruction with Nitrogen isotopes and Stable Isotope Analyses in R. Breast milk seems to have been the most important protein source until two years of age with some supplementary food such as tropical fruits and root cultigens likely introduced earlier. After two, juvenile diets were likely continuously supplemented by starch rich foods such as root cultigens and legumes. By the age of three, the model results suggest that the weaning process was completed. Additional indications suggest that animal marine/riverine protein and maize, while part of the Canímar Abajo female diets, were likely not used to supplement juvenile diets. The combined use of both models here provided a more complete assessment of the weaning process for an ancient Caribbean population, indicating not only the start and end ages of weaning but also the relative importance of different food sources for different age juveniles.
Reconstructing the massive black hole cosmic history through gravitational waves
International Nuclear Information System (INIS)
Sesana, Alberto; Gair, Jonathan; Berti, Emanuele; Volonteri, Marta
2011-01-01
The massive black holes we observe in galaxies today are the natural end-product of a complex evolutionary path, in which black holes seeded in proto-galaxies at high redshift grow through cosmic history via a sequence of mergers and accretion episodes. Electromagnetic observations probe a small subset of the population of massive black holes (namely, those that are active or those that are very close to us), but planned space-based gravitational wave observatories such as the Laser Interferometer Space Antenna (LISA) can measure the parameters of 'electromagnetically invisible' massive black holes out to high redshift. In this paper we introduce a Bayesian framework to analyze the information that can be gathered from a set of such measurements. Our goal is to connect a set of massive black hole binary merger observations to the underlying model of massive black hole formation. In other words, given a set of observed massive black hole coalescences, we assess what information can be extracted about the underlying massive black hole population model. For concreteness we consider ten specific models of massive black hole formation, chosen to probe four important (and largely unconstrained) aspects of the input physics used in structure formation simulations: seed formation, metallicity ''feedback'', accretion efficiency and accretion geometry. For the first time we allow for the possibility of 'model mixing', by drawing the observed population from some combination of the 'pure' models that have been simulated. A Bayesian analysis allows us to recover a posterior probability distribution for the ''mixing parameters'' that characterize the fractions of each model represented in the observed distribution. Our work shows that LISA has enormous potential to probe the underlying physics of structure formation.
Assessment of phylogenetic sensitivity for reconstructing HIV-1 epidemiological relationships.
Beloukas, Apostolos; Magiorkinis, Emmanouil; Magiorkinis, Gkikas; Zavitsanou, Asimina; Karamitros, Timokratis; Hatzakis, Angelos; Paraskevis, Dimitrios
2012-06-01
Phylogenetic analysis has been extensively used as a tool for the reconstruction of epidemiological relations for research or for forensic purposes. It was our objective to assess the sensitivity of different phylogenetic methods and various phylogenetic programs to reconstruct epidemiological links among HIV-1 infected patients that is the probability to reveal a true transmission relationship. Multiple datasets (90) were prepared consisting of HIV-1 sequences in protease (PR) and partial reverse transcriptase (RT) sampled from patients with documented epidemiological relationship (target population), and from unrelated individuals (control population) belonging to the same HIV-1 subtype as the target population. Each dataset varied regarding the number, the geographic origin and the transmission risk groups of the sequences among the control population. Phylogenetic trees were inferred by neighbor-joining (NJ), maximum likelihood heuristics (hML) and Bayesian methods. All clusters of sequences belonging to the target population were correctly reconstructed by NJ and Bayesian methods receiving high bootstrap and posterior probability (PP) support, respectively. On the other hand, TreePuzzle failed to reconstruct or provide significant support for several clusters; high puzzling step support was associated with the inclusion of control sequences from the same geographic area as the target population. In contrary, all clusters were correctly reconstructed by hML as implemented in PhyML 3.0 receiving high bootstrap support. We report that under the conditions of our study, hML using PhyML, NJ and Bayesian methods were the most sensitive for the reconstruction of epidemiological links mostly from sexually infected individuals. Copyright © 2012 Elsevier B.V. All rights reserved.
Ryan W. McEwan; Todd F. Hutchinson; Robert D. Ford; Brian C. McCarthy
2007-01-01
Dendrochronological analysis of fire scars on tree cross sections has been critically important for understanding historical fire regimes and has influenced forest management practices. Despite its value as a tool for understanding historical ecosystems, tree-ring-based fire history reconstruction has rarely been experimentally evaluated. To examine the efficacy of...
Reck-Kortmann, Maikel; Silva-Arias, Gustavo Adolfo; Segatto, Ana Lúcia Anversa; Mäder, Geraldo; Bonatto, Sandro Luis; de Freitas, Loreta Brandão
2014-12-01
The phylogeny of Petunia species has been difficult to resolve, primarily due to the recent diversification of the genus. Several studies have included molecular data in phylogenetic reconstructions of this genus, but all of them have failed to include all taxa and/or analyzed few genetic markers. In the present study, we employed the most inclusive genetic and taxonomic datasets for the genus, aiming to reconstruct the evolutionary history of Petunia based on molecular phylogeny, biogeographic distribution, and character evolution. We included all 20 Petunia morphological species or subspecies in these analyses. Based on nine nuclear and five plastid DNA markers, our phylogenetic analysis reinforces the monophyly of the genus Petunia and supports the hypothesis that the basal divergence is more related to the differentiation of corolla tube length, whereas the geographic distribution of species is more related to divergences within these main clades. Ancestral area reconstructions suggest the Pampas region as the area of origin and earliest divergence in Petunia. The state reconstructions suggest that the ancestor of Petunia might have had a short corolla tube and a bee pollination floral syndrome. Copyright © 2014 Elsevier Inc. All rights reserved.
Sue, Gloria R; Lee, Gordon K
2018-05-01
Mastectomy skin necrosis is a significant problem after breast reconstruction. We sought to perform a comparative analysis on this complication between patients undergoing autologous breast reconstruction and patients undergoing 2-stage expander implant breast reconstruction. A retrospective review was performed on consecutive patients undergoing autologous breast reconstruction or 2-stage expander implant breast reconstruction by the senior author from 2006 through 2015. Patient demographic factors including age, body mass index, history of diabetes, history of smoking, and history of radiation to the breast were collected. Our primary outcome measure was mastectomy skin necrosis. Fisher exact test was used for statistical analysis between the 2 patient cohorts. The treatment patterns of mastectomy skin necrosis were then analyzed. We identified 204 patients who underwent autologous breast reconstruction and 293 patients who underwent 2-stage expander implant breast reconstruction. Patients undergoing autologous breast reconstruction were older, heavier, more likely to have diabetes, and more likely to have had prior radiation to the breast compared with patients undergoing implant-based reconstruction. The incidence of mastectomy skin necrosis was 30.4% of patients in the autologous group compared with only 10.6% of patients in the tissue expander group (P care in the autologous group, only 3.2% were treated with local wound care in the tissue expander group (P skin necrosis is significantly more likely to occur after autologous breast reconstruction compared with 2-stage expander implant-based breast reconstruction. Patients with autologous reconstructions are more readily treated with local wound care compared with patients with tissue expanders, who tended to require operative treatment of this complication. Patients considering breast reconstruction should be counseled appropriately regarding the differences in incidence and management of mastectomy skin
Reconstruction of CT images by the Bayes- back projection method
Haruyama, M; Takase, M; Tobita, H
2002-01-01
In the course of research on quantitative assay of non-destructive measurement of radioactive waste, the have developed a unique program based on the Bayesian theory for reconstruction of transmission computed tomography (TCT) image. The reconstruction of cross-section images in the CT technology usually employs the Filtered Back Projection method. The new imaging reconstruction program reported here is based on the Bayesian Back Projection method, and it has a function of iterative improvement images by every step of measurement. Namely, this method has the capability of prompt display of a cross-section image corresponding to each angled projection data from every measurement. Hence, it is possible to observe an improved cross-section view by reflecting each projection data in almost real time. From the basic theory of Baysian Back Projection method, it can be not only applied to CT types of 1st, 2nd, and 3rd generation. This reported deals with a reconstruction program of cross-section images in the CT of ...
Krishnan, Neeraja M; Seligmann, Hervé; Stewart, Caro-Beth; De Koning, A P Jason; Pollock, David D
2004-10-01
Reconstruction of ancestral DNA and amino acid sequences is an important means of inferring information about past evolutionary events. Such reconstructions suggest changes in molecular function and evolutionary processes over the course of evolution and are used to infer adaptation and convergence. Maximum likelihood (ML) is generally thought to provide relatively accurate reconstructed sequences compared to parsimony, but both methods lead to the inference of multiple directional changes in nucleotide frequencies in primate mitochondrial DNA (mtDNA). To better understand this surprising result, as well as to better understand how parsimony and ML differ, we constructed a series of computationally simple "conditional pathway" methods that differed in the number of substitutions allowed per site along each branch, and we also evaluated the entire Bayesian posterior frequency distribution of reconstructed ancestral states. We analyzed primate mitochondrial cytochrome b (Cyt-b) and cytochrome oxidase subunit I (COI) genes and found that ML reconstructs ancestral frequencies that are often more different from tip sequences than are parsimony reconstructions. In contrast, frequency reconstructions based on the posterior ensemble more closely resemble extant nucleotide frequencies. Simulations indicate that these differences in ancestral sequence inference are probably due to deterministic bias caused by high uncertainty in the optimization-based ancestral reconstruction methods (parsimony, ML, Bayesian maximum a posteriori). In contrast, ancestral nucleotide frequencies based on an average of the Bayesian set of credible ancestral sequences are much less biased. The methods involving simpler conditional pathway calculations have slightly reduced likelihood values compared to full likelihood calculations, but they can provide fairly unbiased nucleotide reconstructions and may be useful in more complex phylogenetic analyses than considered here due to their speed and
Gaussian mixture models and semantic gating improve reconstructions from human brain activity
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Sanne eSchoenmakers
2015-01-01
Full Text Available Better acquisition protocols and analysis techniques are making it possible to use fMRI to obtain highly detailed visualizations of brain processes. In particular we focus on the reconstruction of natural images from BOLD responses in visual cortex. We expand our linear Gaussian framework for percept decoding with Gaussian mixture models to better represent the prior distribution of natural images. Reconstruction of such images then boils down to probabilistic inference in a hybrid Bayesian network. In our set-up, different mixture components correspond to different character categories. Our framework can automatically infer higher-order semantic categories from lower-level brain areas. Furthermore the framework can gate semantic information from higher-order brain areas to enforce the correct category during reconstruction. When categorical information is not available, we show that automatically learned clusters in the data give a similar improvement in reconstruction. The hybrid Bayesian network leads to highly accurate reconstructions in both supervised and unsupervised settings.
Accelerated median root prior reconstruction for pinhole single-photon emission tomography (SPET)
Energy Technology Data Exchange (ETDEWEB)
Sohlberg, Antti [Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, PO Box 1777 FIN-70211, Kuopio (Finland); Ruotsalainen, Ulla [Institute of Signal Processing, DMI, Tampere University of Technology, PO Box 553 FIN-33101, Tampere (Finland); Watabe, Hiroshi [National Cardiovascular Center Research Institute, 5-7-1 Fujisihro-dai, Suita City, Osaka 565-8565 (Japan); Iida, Hidehiro [National Cardiovascular Center Research Institute, 5-7-1 Fujisihro-dai, Suita City, Osaka 565-8565 (Japan); Kuikka, Jyrki T [Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, PO Box 1777 FIN-70211, Kuopio (Finland)
2003-07-07
Pinhole collimation can be used to improve spatial resolution in SPET. However, the resolution improvement is achieved at the cost of reduced sensitivity, which leads to projection images with poor statistics. Images reconstructed from these projections using the maximum likelihood expectation maximization (ML-EM) algorithms, which have been used to reduce the artefacts generated by the filtered backprojection (FBP) based reconstruction, suffer from noise/bias trade-off: noise contaminates the images at high iteration numbers, whereas early abortion of the algorithm produces images that are excessively smooth and biased towards the initial estimate of the algorithm. To limit the noise accumulation we propose the use of the pinhole median root prior (PH-MRP) reconstruction algorithm. MRP is a Bayesian reconstruction method that has already been used in PET imaging and shown to possess good noise reduction and edge preservation properties. In this study the PH-MRP algorithm was accelerated with the ordered subsets (OS) procedure and compared to the FBP, OS-EM and conventional Bayesian reconstruction methods in terms of noise reduction, quantitative accuracy, edge preservation and visual quality. The results showed that the accelerated PH-MRP algorithm was very robust. It provided visually pleasing images with lower noise level than the FBP or OS-EM and with smaller bias and sharper edges than the conventional Bayesian methods.
Profile reconstruction from neutron reflectivity data and a priori knowledge
International Nuclear Information System (INIS)
Leeb, H.
2008-01-01
The problem of incomplete and noisy information in profile reconstruction from neutron reflectometry data is considered. In particular methods of Bayesian statistics in combination with modelling or inverse scattering techniques are considered in order to properly include the required a priori knowledge to obtain quantitatively reliable estimates of the reconstructed profiles. Applying Bayes theorem the results of different experiments on the same sample can be consistently included in the profile reconstruction
Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory
Gopnik, Alison; Wellman, Henry M.
2012-01-01
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…
Bayesian PET image reconstruction incorporating anato-functional joint entropy
International Nuclear Information System (INIS)
Tang Jing; Rahmim, Arman
2009-01-01
We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realistically simulated PET and MR human brain phantoms, the quantitative performance of the proposed algorithm was investigated. Incorporation of the anatomic information via this technique, after parameter optimization, was seen to dramatically improve the noise versus bias tradeoff in every region of interest, compared to the result from using conventional MAP reconstruction. In particular, hot lesions in the FDG PET image, which had no anatomical correspondence in the MR image, also had improved contrast versus noise tradeoff. Corrections were made to figures 3, 4 and 6, and to the second paragraph of section 3.1 on 13 November 2009. The corrected electronic version is identical to the print version.
Comparing and improving reconstruction methods for proxies based on compositional data
Nolan, C.; Tipton, J.; Booth, R.; Jackson, S. T.; Hooten, M.
2017-12-01
Many types of studies in paleoclimatology and paleoecology involve compositional data. Often, these studies aim to use compositional data to reconstruct an environmental variable of interest; the reconstruction is usually done via the development of a transfer function. Transfer functions have been developed using many different methods. Existing methods tend to relate the compositional data and the reconstruction target in very simple ways. Additionally, the results from different methods are rarely compared. Here we seek to address these two issues. First, we introduce a new hierarchical Bayesian multivariate gaussian process model; this model allows for the relationship between each species in the compositional dataset and the environmental variable to be modeled in a way that captures the underlying complexities. Then, we compare this new method to machine learning techniques and commonly used existing methods. The comparisons are based on reconstructing the water table depth history of Caribou Bog (an ombrotrophic Sphagnum peat bog in Old Town, Maine, USA) from a new 7500 year long record of testate amoebae assemblages. The resulting reconstructions from different methods diverge in both their resulting means and uncertainties. In particular, uncertainty tends to be drastically underestimated by some common methods. These results will help to improve inference of water table depth from testate amoebae. Furthermore, this approach can be applied to test and improve inferences of past environmental conditions from a broad array of paleo-proxies based on compositional data
Fröhlich, H.; Klau, G.W.
2013-01-01
Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies
Duda, Pavel; Zrzavý, Jan
2013-10-01
The origin of the fundamental behavioral differences between humans and our closest living relatives is one of the central issues of evolutionary anthropology. The prominent, chimpanzee-based referential model of early hominin behavior has recently been challenged on the basis of broad multispecies comparisons and newly discovered fossil evidence. Here, we argue that while behavioral data on extant great apes are extremely relevant for reconstruction of ancestral behaviors, these behaviors should be reconstructed trait by trait using formal phylogenetic methods. Using the widely accepted hominoid phylogenetic tree, we perform a series of character optimization analyses using 65 selected life-history and behavioral characters for all extant hominid species. This analysis allows us to reconstruct the character states of the last common ancestors of Hominoidea, Hominidae, and the chimpanzee-human last common ancestor. Our analyses demonstrate that many fundamental behavioral and life-history attributes of hominids (including humans) are evidently ancient and likely inherited from the common ancestor of all hominids. However, numerous behaviors present in extant great apes represent their own terminal autapomorphies (both uniquely derived and homoplastic). Any evolutionary model that uses a single extant species to explain behavioral evolution of early hominins is therefore of limited use. In contrast, phylogenetic reconstruction of ancestral states is able to provide a detailed suite of behavioral, ecological and life-history characters for each hypothetical ancestor. The living great apes therefore play an important role for the confident identification of the traits found in the chimpanzee-human last common ancestor, some of which are likely to represent behaviors of the fossil hominins. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bayesian reconstruction of gravitational wave bursts using chirplets
Millhouse, Margaret; Cornish, Neil J.; Littenberg, Tyson
2018-05-01
The LIGO-Virgo Collaboration uses a variety of techniques to detect and characterize gravitational waves. One approach is to use templates—models for the signals derived from Einstein's equations. Another approach is to extract the signals directly from the coherent response of the detectors in the LIGO-Virgo network. Both approaches played an important role in the first gravitational wave detections. Here we extend the BayesWave analysis algorithm, which reconstructs gravitational wave signals using a collection of continuous wavelets, to use a generalized wavelet family, known as chirplets, that have time-evolving frequency content. Since generic gravitational wave signals have frequency content that evolves in time, a collection of chirplets provides a more compact representation of the signal, resulting in more accurate waveform reconstructions, especially for low signal-to-noise events, and events that occupy a large time-frequency volume.
Palacios, Julia A; Minin, Vladimir N
2013-03-01
Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.
Stapert, Eugénie
2013-01-01
This study explores the role of linguistic data in the reconstruction of Dolgan (pre)history. While most ethno-linguistic groups have a longstanding history and a clear ethnic and linguistic affiliation, the formation of the Dolgans has been a relatively recent development, and their ethnic origins
Robust Learning of High-dimensional Biological Networks with Bayesian Networks
Nägele, Andreas; Dejori, Mathäus; Stetter, Martin
Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.
Inverse problems in the Bayesian framework
International Nuclear Information System (INIS)
Calvetti, Daniela; Somersalo, Erkki; Kaipio, Jari P
2014-01-01
The history of Bayesian methods dates back to the original works of Reverend Thomas Bayes and Pierre-Simon Laplace: the former laid down some of the basic principles on inverse probability in his classic article ‘An essay towards solving a problem in the doctrine of chances’ that was read posthumously in the Royal Society in 1763. Laplace, on the other hand, in his ‘Memoirs on inverse probability’ of 1774 developed the idea of updating beliefs and wrote down the celebrated Bayes’ formula in the form we know today. Although not identified yet as a framework for investigating inverse problems, Laplace used the formalism very much in the spirit it is used today in the context of inverse problems, e.g., in his study of the distribution of comets. With the evolution of computational tools, Bayesian methods have become increasingly popular in all fields of human knowledge in which conclusions need to be drawn based on incomplete and noisy data. Needless to say, inverse problems, almost by definition, fall into this category. Systematic work for developing a Bayesian inverse problem framework can arguably be traced back to the 1980s, (the original first edition being published by Elsevier in 1987), although articles on Bayesian methodology applied to inverse problems, in particular in geophysics, had appeared much earlier. Today, as testified by the articles in this special issue, the Bayesian methodology as a framework for considering inverse problems has gained a lot of popularity, and it has integrated very successfully with many traditional inverse problems ideas and techniques, providing novel ways to interpret and implement traditional procedures in numerical analysis, computational statistics, signal analysis and data assimilation. The range of applications where the Bayesian framework has been fundamental goes from geophysics, engineering and imaging to astronomy, life sciences and economy, and continues to grow. There is no question that Bayesian
Bayesian approach to inverse statistical mechanics
Habeck, Michael
2014-05-01
Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.
The NIFTY way of Bayesian signal inference
International Nuclear Information System (INIS)
Selig, Marco
2014-01-01
We introduce NIFTY, 'Numerical Information Field Theory', a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D 3 PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy
The NIFTy way of Bayesian signal inference
Selig, Marco
2014-12-01
We introduce NIFTy, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTy can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTy as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
Directory of Open Access Journals (Sweden)
Aida T Miró-Herrans
Full Text Available Population migration has played an important role in human evolutionary history and in the patterning of human genetic variation. A deeper and empirically-based understanding of human migration dynamics is needed in order to interpret genetic and archaeological evidence and to accurately reconstruct the prehistoric processes that comprise human evolutionary history. Current empirical estimates of migration include either short time frames (i.e. within one generation or partial knowledge about migration, such as proportion of migrants or distance of migration. An analysis of migration that includes both proportion of migrants and distance, and direction over multiple generations would better inform prehistoric reconstructions. To evaluate human migration, we use GPS coordinates from the place of residence of the Yemeni individuals sampled in our study, their birthplaces and their parents' and grandparents' birthplaces to calculate the proportion of migrants, as well as the distance and direction of migration events between each generation. We test for differences in these values between the generations and identify factors that influence the probability of migration. Our results show that the proportion and distance of migration between females and males is similar within generations. In contrast, the proportion and distance of migration is significantly lower in the grandparents' generation, most likely reflecting the decreasing effect of technology. Based on our results, we calculate the proportion of migration events (0.102 and mean and median distances of migration (96 km and 26 km for the grandparent's generation to represent early times in human evolution. These estimates can serve to set parameter values of demographic models in model-based methods of prehistoric reconstruction, such as approximate Bayesian computation. Our study provides the first empirically-based estimates of human migration over multiple generations in a developing
Assessing the accuracy of ancestral protein reconstruction methods.
Directory of Open Access Journals (Sweden)
Paul D Williams
2006-06-01
Full Text Available The phylogenetic inference of ancestral protein sequences is a powerful technique for the study of molecular evolution, but any conclusions drawn from such studies are only as good as the accuracy of the reconstruction method. Every inference method leads to errors in the ancestral protein sequence, resulting in potentially misleading estimates of the ancestral protein's properties. To assess the accuracy of ancestral protein reconstruction methods, we performed computational population evolution simulations featuring near-neutral evolution under purifying selection, speciation, and divergence using an off-lattice protein model where fitness depends on the ability to be stable in a specified target structure. We were thus able to compare the thermodynamic properties of the true ancestral sequences with the properties of "ancestral sequences" inferred by maximum parsimony, maximum likelihood, and Bayesian methods. Surprisingly, we found that methods such as maximum parsimony and maximum likelihood that reconstruct a "best guess" amino acid at each position overestimate thermostability, while a Bayesian method that sometimes chooses less-probable residues from the posterior probability distribution does not. Maximum likelihood and maximum parsimony apparently tend to eliminate variants at a position that are slightly detrimental to structural stability simply because such detrimental variants are less frequent. Other properties of ancestral proteins might be similarly overestimated. This suggests that ancestral reconstruction studies require greater care to come to credible conclusions regarding functional evolution. Inferred functional patterns that mimic reconstruction bias should be reevaluated.
Assessing the accuracy of ancestral protein reconstruction methods.
Williams, Paul D; Pollock, David D; Blackburne, Benjamin P; Goldstein, Richard A
2006-06-23
The phylogenetic inference of ancestral protein sequences is a powerful technique for the study of molecular evolution, but any conclusions drawn from such studies are only as good as the accuracy of the reconstruction method. Every inference method leads to errors in the ancestral protein sequence, resulting in potentially misleading estimates of the ancestral protein's properties. To assess the accuracy of ancestral protein reconstruction methods, we performed computational population evolution simulations featuring near-neutral evolution under purifying selection, speciation, and divergence using an off-lattice protein model where fitness depends on the ability to be stable in a specified target structure. We were thus able to compare the thermodynamic properties of the true ancestral sequences with the properties of "ancestral sequences" inferred by maximum parsimony, maximum likelihood, and Bayesian methods. Surprisingly, we found that methods such as maximum parsimony and maximum likelihood that reconstruct a "best guess" amino acid at each position overestimate thermostability, while a Bayesian method that sometimes chooses less-probable residues from the posterior probability distribution does not. Maximum likelihood and maximum parsimony apparently tend to eliminate variants at a position that are slightly detrimental to structural stability simply because such detrimental variants are less frequent. Other properties of ancestral proteins might be similarly overestimated. This suggests that ancestral reconstruction studies require greater care to come to credible conclusions regarding functional evolution. Inferred functional patterns that mimic reconstruction bias should be reevaluated.
Sana, Furrukh
2016-06-01
Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \\\\ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.
Sana, Furrukh; Ravanelli, Fabio; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim
2016-01-01
Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \\ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.
Directory of Open Access Journals (Sweden)
Benjamin W. Y. Lo
2013-01-01
Full Text Available Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH. Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients. Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs. Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.
Approximate Bayesian evaluations of measurement uncertainty
Possolo, Antonio; Bodnar, Olha
2018-04-01
The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.
Bayesian methods for hackers probabilistic programming and Bayesian inference
Davidson-Pilon, Cameron
2016-01-01
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...
Sparse linear models: Variational approximate inference and Bayesian experimental design
International Nuclear Information System (INIS)
Seeger, Matthias W
2009-01-01
A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.
Sparse linear models: Variational approximate inference and Bayesian experimental design
Energy Technology Data Exchange (ETDEWEB)
Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)
2009-12-01
A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory
Gopnik, Alison; Wellman, Henry M.
2012-01-01
We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but ...
Preston, Jill C.; Kellogg, Elizabeth A.
2006-01-01
Gene duplication is an important mechanism for the generation of evolutionary novelty. Paralogous genes that are not silenced may evolve new functions (neofunctionalization) that will alter the developmental outcome of preexisting genetic pathways, partition ancestral functions (subfunctionalization) into divergent developmental modules, or function redundantly. Functional divergence can occur by changes in the spatio-temporal patterns of gene expression and/or by changes in the activities of their protein products. We reconstructed the evolutionary history of two paralogous monocot MADS-box transcription factors, FUL1 and FUL2, and determined the evolution of sequence and gene expression in grass AP1/FUL-like genes. Monocot AP1/FUL-like genes duplicated at the base of Poaceae and codon substitutions occurred under relaxed selection mostly along the branch leading to FUL2. Following the duplication, FUL1 was apparently lost from early diverging taxa, a pattern consistent with major changes in grass floral morphology. Overlapping gene expression patterns in leaves and spikelets indicate that FUL1 and FUL2 probably share some redundant functions, but that FUL2 may have become temporally restricted under partial subfunctionalization to particular stages of floret development. These data have allowed us to reconstruct the history of AP1/FUL-like genes in Poaceae and to hypothesize a role for this gene duplication in the evolution of the grass spikelet. PMID:16816429
Reconstruction of tritium release history from contaminated groundwater using tree ring analysis
International Nuclear Information System (INIS)
Kalin, R.M.; Murphy, C.E. Jr.; Hall, G.
1995-01-01
The history of tritium releases to the groundwater from buried waste was reconstructed through dendrochronology. Wood from dated tree rings was sectioned from a cross-section of a tree that was thought to tap the groundwater. Cellulose was chemically separated from the wood. The cellulose was combusted and the water of combustion collected for liquid scintillation counting. The tritium concentration in the rings rose rapidly after 1972 which was prior to the first measurements made in this area. Trends in the tritium concentration of water outcropping to the surface are similar to the trends in tritium concentration in tree rings. 14 refs., 3 figs
Directory of Open Access Journals (Sweden)
Thomas Katagis
2014-06-01
Full Text Available In this study, the capability of geographic object-based image analysis (GEOBIA in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS, Thematic Mapper (TM, and Operational Land Imager (OLI images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that “spatial misplacement” was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year fire history in the Mediterranean, based on extended time series of Landsat or similar data.
Bayesian artificial intelligence
Korb, Kevin B
2010-01-01
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente
Nabholz, Benoit; Lartillot, Nicolas
2013-01-01
The nearly neutral theory, which proposes that most mutations are deleterious or close to neutral, predicts that the ratio of nonsynonymous over synonymous substitution rates (dN/dS), and potentially also the ratio of radical over conservative amino acid replacement rates (Kr/Kc), are negatively correlated with effective population size. Previous empirical tests, using life-history traits (LHT) such as body-size or generation-time as proxies for population size, have been consistent with these predictions. This suggests that large-scale phylogenetic reconstructions of dN/dS or Kr/Kc might reveal interesting macroevolutionary patterns in the variation in effective population size among lineages. In this work, we further develop an integrative probabilistic framework for phylogenetic covariance analysis introduced previously, so as to estimate the correlation patterns between dN/dS, Kr/Kc, and three LHT, in mitochondrial genomes of birds and mammals. Kr/Kc displays stronger and more stable correlations with LHT than does dN/dS, which we interpret as a greater robustness of Kr/Kc, compared with dN/dS, the latter being confounded by the high saturation of the synonymous substitution rate in mitochondrial genomes. The correlation of Kr/Kc with LHT was robust when controlling for the potentially confounding effects of nucleotide compositional variation between taxa. The positive correlation of the mitochondrial Kr/Kc with LHT is compatible with previous reports, and with a nearly neutral interpretation, although alternative explanations are also possible. The Kr/Kc model was finally used for reconstructing life-history evolution in birds and mammals. This analysis suggests a fairly large-bodied ancestor in both groups. In birds, life-history evolution seems to have occurred mainly through size reduction in Neoavian birds, whereas in placental mammals, body mass evolution shows disparate trends across subclades. Altogether, our work represents a further step toward a more
Vyas, Deven N; Kitchen, Andrew; Miró-Herrans, Aida T; Pearson, Laurel N; Al-Meeri, Ali; Mulligan, Connie J
2016-03-01
Anatomically, modern humans are thought to have migrated out of Africa ∼60,000 years ago in the first successful global dispersal. This initial migration may have passed through Yemen, a region that has experienced multiple migrations events with Africa and Eurasia throughout human history. We use Bayesian phylogenetics to determine how ancient and recent migrations have shaped Yemeni mitogenomic variation. We sequenced 113 mitogenomes from multiple Yemeni regions with a focus on haplogroups M, N, and L3(xM,N) as these groups have the oldest evolutionary history outside of Africa. We performed Bayesian evolutionary analyses to generate time-measured phylogenies calibrated by Neanderthal and Denisovan mitogenomes in order to determine the age of Yemeni-specific clades. As defined by Yemeni monophyly, Yemeni in situ evolution is limited to the Holocene or latest Pleistocene (ages of clades in subhaplogroups L3b1a1a, L3h2, L3x1, M1a1f, M1a5, N1a1a3, and N1a3 range from 2 to 14 kya) and is often situated within broader Horn of Africa/southern Arabia in situ evolution (L3h2, L3x1, M1a1f, M1a5, and N1a1a3 ages range from 7 to 29 kya). Five subhaplogroups show no monophyly and are candidates for Holocene migration into Yemen (L0a2a2a, L3d1a1a, L3i2, M1a1b, and N1b1a). Yemeni mitogenomes are largely the product of Holocene migration, and subsequent in situ evolution, from Africa and western Eurasia. However, we hypothesize that recent population movements may obscure the genetic signature of more ancient migrations. Additional research, e.g., analyses of Yemeni nuclear genetic data, is needed to better reconstruct the complex population and migration histories associated with Out of Africa. © 2015 Wiley Periodicals, Inc.
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...
Directory of Open Access Journals (Sweden)
Pablo Fresia
Full Text Available Insect pest phylogeography might be shaped both by biogeographic events and by human influence. Here, we conducted an approximate Bayesian computation (ABC analysis to investigate the phylogeography of the New World screwworm fly, Cochliomyia hominivorax, with the aim of understanding its population history and its order and time of divergence. Our ABC analysis supports that populations spread from North to South in the Americas, in at least two different moments. The first split occurred between the North/Central American and South American populations in the end of the Last Glacial Maximum (15,300-19,000 YBP. The second split occurred between the North and South Amazonian populations in the transition between the Pleistocene and the Holocene eras (9,100-11,000 YBP. The species also experienced population expansion. Phylogenetic analysis likewise suggests this north to south colonization and Maxent models suggest an increase in the number of suitable areas in South America from the past to present. We found that the phylogeographic patterns observed in C. hominivorax cannot be explained only by climatic oscillations and can be connected to host population histories. Interestingly we found these patterns are very coincident with general patterns of ancient human movements in the Americas, suggesting that humans might have played a crucial role in shaping the distribution and population structure of this insect pest. This work presents the first hypothesis test regarding the processes that shaped the current phylogeographic structure of C. hominivorax and represents an alternate perspective on investigating the problem of insect pests.
The subjectivity of scientists and the Bayesian approach
Press, James S
2001-01-01
Comparing and contrasting the reality of subjectivity in the work of history's great scientists and the modern Bayesian approach to statistical analysisScientists and researchers are taught to analyze their data from an objective point of view, allowing the data to speak for themselves rather than assigning them meaning based on expectations or opinions. But scientists have never behaved fully objectively. Throughout history, some of our greatest scientific minds have relied on intuition, hunches, and personal beliefs to make sense of empirical data-and these subjective influences have often a
Reconstructing the invasion history of Heracleum persicum (Apiaceae) into Europe
Czech Academy of Sciences Publication Activity Database
Rijal, D. P.; Alm, T.; Jahodová, Šárka; Stenoien, H. K.; Alsos, I. G.
2015-01-01
Roč. 24, č. 22 (2015), s. 5522-5543 ISSN 0962-1083 Institutional support: RVO:67985939 Keywords : approximate Bayesian computation * genetic variation * population genetics Subject RIV: EH - Ecology, Behaviour Impact factor: 5.947, year: 2015
A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction
De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David
2016-01-01
Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. PMID:25281847
A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction.
De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David
2016-05-01
Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.
Bayesian artificial intelligence
Korb, Kevin B
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
Czech Academy of Sciences Publication Activity Database
Fernandes, R.; Eley, Y.; Brabec, Marek; Lucquin, A.; Millard, A.; Craig, O.E.
2018-01-01
Roč. 117, March (2018), s. 31-42 ISSN 0146-6380 Institutional support: RVO:67985807 Keywords : Fatty acids * carbon isotopes * pottery use * Bayesian mixing models * FRUITS Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.081, year: 2016
Bayesian soft x-ray tomography and MHD mode analysis on HL-2A
Li, Dong; Liu, Yi; Svensson, J.; Liu, Y. Q.; Song, X. M.; Yu, L. M.; Mao, Rui; Fu, B. Z.; Deng, Wei; Yuan, B. S.; Ji, X. Q.; Xu, Yuan; Chen, Wei; Zhou, Yan; Yang, Q. W.; Duan, X. R.; Liu, Yong; HL-2A Team
2016-03-01
A Bayesian based tomography method using so-called Gaussian processes (GPs) for the emission model has been applied to the soft x-ray (SXR) diagnostics on HL-2A tokamak. To improve the accuracy of reconstructions, the standard GP is extended to a non-stationary version so that different smoothness between the plasma center and the edge can be taken into account in the algorithm. The uncertainty in the reconstruction arising from measurement errors and incapability can be fully analyzed by the usage of Bayesian probability theory. In this work, the SXR reconstructions by this non-stationary Gaussian processes tomography (NSGPT) method have been compared with the equilibrium magnetic flux surfaces, generally achieving a satisfactory agreement in terms of both shape and position. In addition, singular-value-decomposition (SVD) and Fast Fourier Transform (FFT) techniques have been applied for the analysis of SXR and magnetic diagnostics, in order to explore the spatial and temporal features of the saturated long-lived magnetohydrodynamics (MHD) instability induced by energetic particles during neutral beam injection (NBI) on HL-2A. The result shows that this ideal internal kink instability has a dominant m/n = 1/1 mode structure along with a harmonics m/n = 2/2, which are coupled near the q = 1 surface with a rotation frequency of 12 kHz.
Yuan, Ying; MacKinnon, David P.
2009-01-01
This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...
Introduction to Bayesian statistics
Bolstad, William M
2017-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
DEFF Research Database (Denmark)
Poulsen, Bo
2012-01-01
human society and natural marine resources. Within this broad topic, several trends and objectives are discernable. The essay argue that the so-called material marine environmental history has its main focus on trying to reconstruct the presence, development and environmental impact of past fisheries......This essay provides an overview of recent trends in the historiography of marine environmental history, a sub-field of environmental history which has grown tremendously in scope and size over the last c. 15 years. The object of marine environmental history is the changing relationship between...... and whaling operations. This ambition often entails a reconstruction also of how marine life has changed over time. The time frame rages from Paleolithicum to the present era. The field of marine environmental history also includes a more culturally oriented environmental history, which mainly has come...
Bayesian network modelling of upper gastrointestinal bleeding
Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri
2013-09-01
Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.
Bayesian Reasoning Using 3D Relations for Lane Marker Detection
DEFF Research Database (Denmark)
Boesman, Bart; Jensen, Lars Baunegaard With; Baseski, Emre
2009-01-01
We introduce a lane marker detection algorithm that integrates 3D attributes as well as 3D relations between local edges and semi-global contours in a Bayesian framework. The algorithm is parameter free and does not make use of any heuristic assumptions. The reasoning is based on the complete...... to the reconstruction process need to be taken into account to make the reasoning process more stable. The results are shown on a publicly available data set....
Koblmüller, Stephan; Wayne, Robert K; Leonard, Jennifer A
2012-08-23
Recurrent cycles of climatic change during the Quaternary period have dramatically affected the population genetic structure of many species. We reconstruct the recent demographic history of the coyote (Canis latrans) through the use of Bayesian techniques to examine the effects of Late Quaternary climatic perturbations on the genetic structure of a highly mobile generalist species. Our analysis reveals a lack of phylogeographic structure throughout the range but past population size changes correlated with climatic changes. We conclude that even generalist carnivorous species are very susceptible to environmental changes associated with climatic perturbations. This effect may be enhanced in coyotes by interspecific competition with larger carnivores.
DEFF Research Database (Denmark)
Harkins, Catriona P; Pichon, Bruno; Doumith, Michel
2017-01-01
element, was horizontally transferred to an intrinsically sensitive strain of S. aureus. RESULTS: Whole genome sequencing a collection of the first MRSA isolates allows us to reconstruct the evolutionary history of the archetypal MRSA. We apply Bayesian phylogenetic reconstruction to infer the time point...
Heiler, Katharina; Vaate, Abraham bij de; Ekschmitt, Klemens; Oheimb, Parm von; Albrecht, Christian; Wilke, Thomas
2013-01-01
The recent introduction of the quagga mussel into Western European freshwaters marked the beginning of one of the most successful biological invasions during the past years in this region. However, the spatial and temporal origin of the first invasive population(s) in Western Europe as well as subsequent spreading routes still remain under discussion. In this study, we therefore aim at reconstructing the early invasion history of the quagga mussel in Western Europe based on an age-corrected t...
Bayesian tomography and integrated data analysis in fusion diagnostics
Li, Dong; Dong, Y. B.; Deng, Wei; Shi, Z. B.; Fu, B. Z.; Gao, J. M.; Wang, T. B.; Zhou, Yan; Liu, Yi; Yang, Q. W.; Duan, X. R.
2016-11-01
In this article, a Bayesian tomography method using non-stationary Gaussian process for a prior has been introduced. The Bayesian formalism allows quantities which bear uncertainty to be expressed in the probabilistic form so that the uncertainty of a final solution can be fully resolved from the confidence interval of a posterior probability. Moreover, a consistency check of that solution can be performed by checking whether the misfits between predicted and measured data are reasonably within an assumed data error. In particular, the accuracy of reconstructions is significantly improved by using the non-stationary Gaussian process that can adapt to the varying smoothness of emission distribution. The implementation of this method to a soft X-ray diagnostics on HL-2A has been used to explore relevant physics in equilibrium and MHD instability modes. This project is carried out within a large size inference framework, aiming at an integrated analysis of heterogeneous diagnostics.
Sparse Bayesian Learning for DOA Estimation with Mutual Coupling
Directory of Open Access Journals (Sweden)
Jisheng Dai
2015-10-01
Full Text Available Sparse Bayesian learning (SBL has given renewed interest to the problem of direction-of-arrival (DOA estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs. Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.
The resolved star formation history of M51a through successive Bayesian marginalization
Martínez-García, Eric E.; Bruzual, Gustavo; Magris C., Gladis; González-Lópezlira, Rosa A.
2018-02-01
We have obtained the time and space-resolved star formation history (SFH) of M51a (NGC 5194) by fitting Galaxy Evolution Explorer (GALEX), Sloan Digital Sky Survey and near-infrared pixel-by-pixel photometry to a comprehensive library of stellar population synthesis models drawn from the Synthetic Spectral Atlas of Galaxies (SSAG). We fit for each space-resolved element (pixel) an independent model where the SFH is averaged in 137 age bins, each one 100 Myr wide. We used the Bayesian Successive Priors (BSP) algorithm to mitigate the bias in the present-day spatial mass distribution. We test BSP with different prior probability distribution functions (PDFs); this exercise suggests that the best prior PDF is the one concordant with the spatial distribution of the stellar mass as inferred from the near-infrared images. We also demonstrate that varying the implicit prior PDF of the SFH in SSAG does not affect the results. By summing the contributions to the global star formation rate of each pixel, at each age bin, we have assembled the resolved SFH of the whole galaxy. According to these results, the star formation rate of M51a was exponentially increasing for the first 10 Gyr after the big bang, and then turned into an exponentially decreasing function until the present day. Superimposed, we find a main burst of star formation at t ≈ 11.9 Gyr after the big bang.
International Nuclear Information System (INIS)
Wei, Hao; Nan Zhang, Shuang
2009-01-01
Recently, Gamma-Ray Bursts (GRBs) were proposed to be a complementary cosmological probe to type Ia supernovae (SNIa). GRBs have been advocated to be standard candles since several empirical GRB luminosity relations were proposed as distance indicators. However, there is a so-called circularity problem in the direct use of GRBs. Recently, a new idea to calibrate GRBs in a completely cosmology independent manner has been proposed, and the circularity problem can be solved. In the present work, following the method proposed by Liang et al., we calibrate 70 GRBs with the Amati relation using 307 SNIa. Then, following the method proposed by Shafieloo et al., we smoothly reconstruct the cosmic expansion history up to redshift z=6.29 with the calibrated GRBs. We find some new features in the reconstructed results. (orig.)
Directory of Open Access Journals (Sweden)
Sinisa Pajevic
2009-01-01
Full Text Available Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB and Posterior Weighted Averaging (PWA methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics.
Bayesian maximum posterior probability method for interpreting plutonium urinalysis data
International Nuclear Information System (INIS)
Miller, G.; Inkret, W.C.
1996-01-01
A new internal dosimetry code for interpreting urinalysis data in terms of radionuclide intakes is described for the case of plutonium. The mathematical method is to maximise the Bayesian posterior probability using an entropy function as the prior probability distribution. A software package (MEMSYS) developed for image reconstruction is used. Some advantages of the new code are that it ensures positive calculated dose, it smooths out fluctuating data, and it provides an estimate of the propagated uncertainty in the calculated doses. (author)
A combined reconstruction-classification method for diffuse optical tomography
Energy Technology Data Exchange (ETDEWEB)
Hiltunen, P [Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, PO Box 3310, FI-02015 TKK (Finland); Prince, S J D; Arridge, S [Department of Computer Science, University College London, Gower Street London, WC1E 6B (United Kingdom)], E-mail: petri.hiltunen@tkk.fi, E-mail: s.prince@cs.ucl.ac.uk, E-mail: s.arridge@cs.ucl.ac.uk
2009-11-07
We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.
Bayesian data assimilation for stochastic multiscale models of transport in porous media.
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M. (Massachusetts Institute of Technology, Cambridge, MA); van Bloemen Waanders, Bart Gustaaf (Sandia National Laboratories, Albuquerque NM); Parno, Matthew (Massachusetts Institute of Technology, Cambridge, MA); Ray, Jaideep; Lefantzi, Sophia; Salazar, Luke (Sandia National Laboratories, Albuquerque NM); McKenna, Sean Andrew (Sandia National Laboratories, Albuquerque NM); Klise, Katherine A. (Sandia National Laboratories, Albuquerque NM)
2011-10-01
We investigate Bayesian techniques that can be used to reconstruct field variables from partial observations. In particular, we target fields that exhibit spatial structures with a large spectrum of lengthscales. Contemporary methods typically describe the field on a grid and estimate structures which can be resolved by it. In contrast, we address the reconstruction of grid-resolved structures as well as estimation of statistical summaries of subgrid structures, which are smaller than the grid resolution. We perform this in two different ways (a) via a physical (phenomenological), parameterized subgrid model that summarizes the impact of the unresolved scales at the coarse level and (b) via multiscale finite elements, where specially designed prolongation and restriction operators establish the interscale link between the same problem defined on a coarse and fine mesh. The estimation problem is posed as a Bayesian inverse problem. Dimensionality reduction is performed by projecting the field to be inferred on a suitable orthogonal basis set, viz. the Karhunen-Loeve expansion of a multiGaussian. We first demonstrate our techniques on the reconstruction of a binary medium consisting of a matrix with embedded inclusions, which are too small to be grid-resolved. The reconstruction is performed using an adaptive Markov chain Monte Carlo method. We find that the posterior distributions of the inferred parameters are approximately Gaussian. We exploit this finding to reconstruct a permeability field with long, but narrow embedded fractures (which are too fine to be grid-resolved) using scalable ensemble Kalman filters; this also allows us to address larger grids. Ensemble Kalman filtering is then used to estimate the values of hydraulic conductivity and specific yield in a model of the High Plains Aquifer in Kansas. Strong conditioning of the spatial structure of the parameters and the non-linear aspects of the water table aquifer create difficulty for the ensemble Kalman
EEG-fMRI Bayesian framework for neural activity estimation: a simulation study
Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo
2016-12-01
Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.
Lesaffre, Emmanuel
2012-01-01
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd
Bayesian data analysis for newcomers.
Kruschke, John K; Liddell, Torrin M
2018-02-01
This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.
A Taxonomic Reduced-Space Pollen Model for Paleoclimate Reconstruction
Wahl, E. R.; Schoelzel, C.
2010-12-01
Paleoenvironmental reconstruction from fossil pollen often attempts to take advantage of the rich taxonomic diversity in such data. Here, a taxonomically "reduced-space" reconstruction model is explored that would be parsimonious in introducing parameters needing to be estimated within a Bayesian Hierarchical Modeling context. This work involves a refinement of the traditional pollen ratio method. This method is useful when one (or a few) dominant pollen type(s) in a region have a strong positive correlation with a climate variable of interest and another (or a few) dominant pollen type(s) have a strong negative correlation. When, e.g., counts of pollen taxa a and b (r >0) are combined with pollen types c and d (r logistic generalized linear model (GLM). The GLM can readily model this relationship in the forward form, pollen = g(climate), which is more physically realistic than inverse models often used in paleoclimate reconstruction [climate = f(pollen)]. The specification of the model is: rnum Bin(n,p), where E(r|T) = p = exp(η)/[1+exp(η)], and η = α + β(T); r is the pollen ratio formed as above, rnum is the ratio numerator, n is the ratio denominator (i.e., the sum of pollen counts), the denominator-specific count is (n - rnum), and T is the temperature at each site corresponding to a specific value of r. Ecological and empirical screening identified the model (Spruce+Birch) / (Spruce+Birch+Oak+Hickory) for use in temperate eastern N. America. α and β were estimated using both "traditional" and Bayesian GLM algorithms (in R). Although it includes only four pollen types, the ratio model yields more explained variation ( 80%) in the pollen-temperature relationship of the study region than a 64-taxon modern analog technique (MAT). Thus, the new pollen ratio method represents an information-rich, reduced space data model that can be efficiently employed in a BHM framework. The ratio model can directly reconstruct past temperature by solving the GLM equations
Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.
Zhang, Guanglei; Liu, Fei; Liu, Jie; Luo, Jianwen; Xie, Yaoqin; Bai, Jing; Xing, Lei
2017-01-01
X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.
Posterior consistency for Bayesian inverse problems through stability and regression results
International Nuclear Information System (INIS)
Vollmer, Sebastian J
2013-01-01
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is described via a probability measure. The joint distribution of the unknown input and the data is then conditioned, using Bayes’ formula, giving rise to the posterior distribution on the unknown input. In this setting we prove posterior consistency for nonlinear inverse problems: a sequence of data is considered, with diminishing fluctuations around a single truth and it is then of interest to show that the resulting sequence of posterior measures arising from this sequence of data concentrates around the truth used to generate the data. Posterior consistency justifies the use of the Bayesian approach very much in the same way as error bounds and convergence results for regularization techniques do. As a guiding example, we consider the inverse problem of reconstructing the diffusion coefficient from noisy observations of the solution to an elliptic PDE in divergence form. This problem is approached by splitting the forward operator into the underlying continuum model and a simpler observation operator based on the output of the model. In general, these splittings allow us to conclude posterior consistency provided a deterministic stability result for the underlying inverse problem and a posterior consistency result for the Bayesian regression problem with the push-forward prior. Moreover, we prove posterior consistency for the Bayesian regression problem based on the regularity, the tail behaviour and the small ball probabilities of the prior. (paper)
Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)
DEFF Research Database (Denmark)
Stahlhut, Carsten; Mørup, Morten; Winther, Ole
2009-01-01
In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface, and ele......In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface...
Space Shuttle RTOS Bayesian Network
Morris, A. Terry; Beling, Peter A.
2001-01-01
With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores
Reconstructions of human history by mapping dental markers in living Eurasian populations
Kashibadze, Vera F.; Nasonova, Olga G.; Nasonov, Dmitry S.
2013-01-01
Using advances in gene geography and anthropophenetics, the phenogeographical method for anthropological research was initiated and developed using dental data. Statistical and cartographical analyses are provided for 498 living Eurasian populations. Mapping principal components supplied evidence for the phene pool structure in Eurasian populations, and for reconstructions of Homo sapiens history on the continent. Longitudinal variability seems to be the most important regularity revealed by principal components analysis (PCA) and mapping, indicating the division of the whole area into western and eastern main provinces. So, the most ancient scenario in the history of Eurasian populations developed from two perspective different groups: a western group related to ancient populations of West Asia and an eastern one rooted in ancestry in South and/or East Asia. In spite of the enormous territory and the revealed divergence, the populations of the continent have undergone wide scale and intensive timeespace interaction. Many details in the revealed landscapes are background to different historical events. Migrations and assimilation are two essential phenomena in Eurasian history: the widespread of the western combination through the whole continent to the Pacific coastline and the movement of the paradoxical combinations of eastern and western markers from South or Central Asia to the east and west. Taking into account that no additional eastern combinations in the total variation in Asian groups have been found, but that mixed or western markers' sets and that eastern dental characteristics are traced in Asia since Homo erectus, the assumption is made in favour of the hetero-level assimilation in the eastern province and of net-like evolution of H. sapiens.
Improved convergence of gradient-based reconstruction using multi-scale models
International Nuclear Information System (INIS)
Cunningham, G.S.; Hanson, K.M.; Koyfman, I.
1996-01-01
Geometric models have received increasing attention in medical imaging for tasks such as segmentation, reconstruction, restoration, and registration. In order to determine the best configuration of the geometric model in the context of any of these tasks, one needs to perform a difficult global optimization of an energy function that may have many local minima. Explicit models of geometry, also called deformable models, snakes, or active contours, have been used extensively to solve image segmentation problems in a non-Bayesian framework. Researchers have seen empirically that multi-scale analysis is useful for convergence to a configuration that is near the global minimum. In this type of analysis, the image data are convolved with blur functions of increasing resolution, and an optimal configuration of the snake is found for each blurred image. The configuration obtained using the highest resolution blur is used as the solution to the global optimization problem. In this article, the authors use explicit models of geometry for a variety of Bayesian estimation problems, including image segmentation, reconstruction and restoration. The authors introduce a multi-scale approach that blurs the geometric model, rather than the image data, and show that this approach turns a global, highly nonquadratic optimization into a sequence of local, approximately quadratic problems that converge to the global minimum. The result is a deterministic, robust, and efficient optimization strategy applicable to a wide variety of Bayesian estimation problems in which geometric models of images are an important component
Bayesian methods for data analysis
Carlin, Bradley P.
2009-01-01
Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors
Directory of Open Access Journals (Sweden)
John Heraty
2013-10-01
Full Text Available The immature stages and behavior of Pseudometagea schwarzii (Ashmead (Hymenoptera: Eucharitidae: Eucharitini are described, and the presence of an endoparasitic planidium that undergoes growth-feeding in the larva of the host ant (Lasius neoniger Emery is confirmed. Bayesian inference and parsimony ancestral state reconstruction are used to map the evolution of endoparasitism across the eucharitid-perilampid clade. Endoparasitism is proposed to have evolved independently three times within Eucharitidae, including once in Pseudometagea Ashmead, and at least twice in Perilampus Latreille. Endoparasitism is independent as an evolutionary trait from other life history traits such as differences in growth and development of the first-instar larva, hypermetamorphic larval morphology, and other biological traits, including koinobiosis.
Stone, Graham N; Lohse, Konrad; Nicholls, James A; Fuentes-Utrilla, Pablo; Sinclair, Frazer; Schönrogge, Karsten; Csóka, György; Melika, George; Nieves-Aldrey, Jose-Luis; Pujade-Villar, Juli; Tavakoli, Majide; Askew, Richard R; Hickerson, Michael J
2012-03-20
How geographically widespread biological communities assemble remains a major question in ecology. Do parallel population histories allow sustained interactions (such as host-parasite or plant-pollinator) among species, or do discordant histories necessarily interrupt them? Though few empirical data exist, these issues are central to our understanding of multispecies evolutionary dynamics. Here we use hierarchical approximate Bayesian analysis of DNA sequence data for 12 herbivores and 19 parasitoids to reconstruct the assembly of an insect community spanning the Western Palearctic and assess the support for alternative host tracking and ecological sorting hypotheses. We show that assembly occurred primarily by delayed host tracking from a shared eastern origin. Herbivores escaped their enemies for millennia before parasitoid pursuit restored initial associations, with generalist parasitoids no better able to track their hosts than specialists. In contrast, ecological sorting played only a minor role. Substantial turnover in host-parasitoid associations means that coevolution must have been diffuse, probably contributing to the parasitoid generalism seen in this and similar systems. Reintegration of parasitoids after host escape shows these communities to have been unsaturated throughout their history, arguing against major roles for parasitoid niche evolution or competition during community assembly. Copyright Â© 2012 Elsevier Ltd. All rights reserved.
Iterative image reconstruction in ECT
International Nuclear Information System (INIS)
Chintu Chen; Ordonez, C.E.; Wernick, M.N.; Aarsvold, J.N.; Gunter, D.L.; Wong, W.H.; Kapp, O.H.; Xiaolong Ouyang; Levenson, M.; Metz, C.E.
1992-01-01
A series of preliminary studies has been performed in the authors laboratories to explore the use of a priori information in Bayesian image restoration and reconstruction. One piece of a priori information is the fact that intensities of neighboring pixels tend to be similar if they belong to the same region within which similar tissue characteristics are exhibited. this property of local continuity can be modeled by the use of Gibbs priors, as first suggested by German and Geman. In their investigation, they also included line sites between each pair of neighboring pixels in the Gibbs prior and used discrete binary numbers to indicate the absence or presence of boundaries between regions. These two features of the a priori model permit averaging within boundaries of homogeneous regions to alleviate the degradation caused by Poisson noise. with the use of this Gibbs prior in combination with the technique of stochastic relaxation, German and Geman demonstrated that noise levels can be reduced significantly in 2-D image restoration. They have developed a Bayesian method that utilizes a Gibbs prior to describe the spatial correlation of neighboring regions and takes into account the effect of limited spatial resolution as well. The statistical framework of the proposed approach is based on the data augmentation scheme suggested by Tanner and Wong. Briefly outlined here, this Bayesian method is based on Geman and Geman's approach
The genetic diversity and evolutionary history of hepatitis C virus in Vietnam.
Li, Chunhua; Yuan, Manqiong; Lu, Ling; Lu, Teng; Xia, Wenjie; Pham, Van H; Vo, An X D; Nguyen, Mindie H; Abe, Kenji
2014-11-01
Vietnam has a unique history in association with foreign countries, which may have resulted in multiple introductions of the alien HCV strains to mix with those indigenous ones. In this study, we characterized the HCV sequences in Core-E1 and NS5B regions from 236 Vietnamese individuals. We identified multiple HCV lineages; 6a, 6 e, 6h, 6k, 6l, 6 o, 6p, and two novel variants may represent the indigenous strains; 1a was probably introduced from the US; 1b and 2a possibly originated in East Asia; while 2i, 2j, and 2m were likely brought by French explorers. We inferred the evolutionary history for four major subtypes: 1a, 1b, 6a, and 6 e. The obtained Bayesian Skyline Plots (BSPs) consistently showed the rapid HCV population growth from 1955 to 1963 until 1984 or after, corresponding to the era of the Vietnam War. We also estimated HCV growth rates and reconstructed phylogeographic trees for comparing subtypes 1a, 1b, and HCV-2. Copyright © 2014 Elsevier Inc. All rights reserved.
The history of the Society of Urodynamics, Female Pelvic Medicine, and Urogenital Reconstruction.
Weissbart, Steven J; Zimmern, Philippe E; Nitti, Victor W; Lemack, Gary E; Kobashi, Kathleen C; Vasavada, Sandip P; Wein, Alan J
2018-03-25
To review the history of the Society of Urodynamics, Female Pelvic Medicine and Urogenital Reconstruction (SUFU). We reviewed Society meeting minutes, contacted all living former Society presidents, searched the William P. Didusch Center for Urology History records, and asked Society members to share their important Society experiences in order to gather important historical information about the Society. The Society initially formed as the Urodynamics Society in 1969 in the backdrop of a growing passion for scientific research in the country after World War II ended. Since then, Society meetings have provided a pivotal forum for the advancement of science in lower urinary tract dysfunction. Meetings occurred annually until 2004, when the meeting schedule increased to biannual. The journal, Neurourology and Urodynamics, became the official journal of the Society in 2005. SUFU has authored important guidelines on urodynamics (2012), non-neurogenic overactive bladder (2012), and stress urinary incontinence (2017) and has shared important collaborations with other societies, including the American Urological Association (AUA), the International Continence Society (ICS), and the International Society of Pelvic Neuromodulation (ISPiN). SUFU has also been instrumental in trainee education and helped to establish formal fellowship training in the field in addition to holding a yearly educational meeting for urology residents. The Society has been led by 21 presidents throughout its history. Throughout the Society's near half-century long existence, the Society has fostered research, published guidelines, and educated trainees in order to improve the care of individuals suffering from lower urinary tract dysfunction. © 2018 Wiley Periodicals, Inc.
Marsman, M.; Wagenmakers, E.-J.
2017-01-01
We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
Bayesian networks for evaluation of evidence from forensic entomology.
Andersson, M Gunnar; Sundström, Anders; Lindström, Anders
2013-09-01
In the aftermath of a CBRN incident, there is an urgent need to reconstruct events in order to bring the perpetrators to court and to take preventive actions for the future. The challenge is to discriminate, based on available information, between alternative scenarios. Forensic interpretation is used to evaluate to what extent results from the forensic investigation favor the prosecutors' or the defendants' arguments, using the framework of Bayesian hypothesis testing. Recently, several new scientific disciplines have been used in a forensic context. In the AniBioThreat project, the framework was applied to veterinary forensic pathology, tracing of pathogenic microorganisms, and forensic entomology. Forensic entomology is an important tool for estimating the postmortem interval in, for example, homicide investigations as a complement to more traditional methods. In this article we demonstrate the applicability of the Bayesian framework for evaluating entomological evidence in a forensic investigation through the analysis of a hypothetical scenario involving suspect movement of carcasses from a clandestine laboratory. Probabilities of different findings under the alternative hypotheses were estimated using a combination of statistical analysis of data, expert knowledge, and simulation, and entomological findings are used to update the beliefs about the prosecutors' and defendants' hypotheses and to calculate the value of evidence. The Bayesian framework proved useful for evaluating complex hypotheses using findings from several insect species, accounting for uncertainty about development rate, temperature, and precolonization. The applicability of the forensic statistic approach to evaluating forensic results from a CBRN incident is discussed.
Bayesian optimization for materials science
Packwood, Daniel
2017-01-01
This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...
Accurate phylogenetic tree reconstruction from quartets: a heuristic approach.
Reaz, Rezwana; Bayzid, Md Shamsuzzoha; Rahman, M Sohel
2014-01-01
Supertree methods construct trees on a set of taxa (species) combining many smaller trees on the overlapping subsets of the entire set of taxa. A 'quartet' is an unrooted tree over 4 taxa, hence the quartet-based supertree methods combine many 4-taxon unrooted trees into a single and coherent tree over the complete set of taxa. Quartet-based phylogeny reconstruction methods have been receiving considerable attentions in the recent years. An accurate and efficient quartet-based method might be competitive with the current best phylogenetic tree reconstruction methods (such as maximum likelihood or Bayesian MCMC analyses), without being as computationally intensive. In this paper, we present a novel and highly accurate quartet-based phylogenetic tree reconstruction method. We performed an extensive experimental study to evaluate the accuracy and scalability of our approach on both simulated and biological datasets.
PET reconstruction via nonlocal means induced prior.
Hou, Qingfeng; Huang, Jing; Bian, Zhaoying; Chen, Wufan; Ma, Jianhua
2015-01-01
The traditional Bayesian priors for maximum a posteriori (MAP) reconstruction methods usually incorporate local neighborhood interactions that penalize large deviations in parameter estimates for adjacent pixels; therefore, only local pixel differences are utilized. This limits their abilities of penalizing the image roughness. To achieve high-quality PET image reconstruction, this study investigates a MAP reconstruction strategy by incorporating a nonlocal means induced (NLMi) prior (NLMi-MAP) which enables utilizing global similarity information of image. The present NLMi prior approximates the derivative of Gibbs energy function by an NLM filtering process. Specially, the NLMi prior is obtained by subtracting the current image estimation from its NLM filtered version and feeding the residual error back to the reconstruction filter to yield the new image estimation. We tested the present NLMi-MAP method with simulated and real PET datasets. Comparison studies with conventional filtered backprojection (FBP) and a few iterative reconstruction methods clearly demonstrate that the present NLMi-MAP method performs better in lowering noise, preserving image edge and in higher signal to noise ratio (SNR). Extensive experimental results show that the NLMi-MAP method outperforms the existing methods in terms of cross profile, noise reduction, SNR, root mean square error (RMSE) and correlation coefficient (CORR).
Understanding Computational Bayesian Statistics
Bolstad, William M
2011-01-01
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
Bayesian networks with examples in R
Scutari, Marco
2014-01-01
Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.
Rumetshofer, M.; Heim, P.; Thaler, B.; Ernst, W. E.; Koch, M.; von der Linden, W.
2018-06-01
Ultrafast dynamical processes in photoexcited molecules can be observed with pump-probe measurements, in which information about the dynamics is obtained from the transient signal associated with the excited state. Background signals provoked by pump and/or probe pulses alone often obscure these excited-state signals. Simple subtraction of pump-only and/or probe-only measurements from the pump-probe measurement, as commonly applied, results in a degradation of the signal-to-noise ratio and, in the case of coincidence detection, the danger of overrated background subtraction. Coincidence measurements additionally suffer from false coincidences, requiring long data-acquisition times to keep erroneous signals at an acceptable level. Here we present a probabilistic approach based on Bayesian probability theory that overcomes these problems. For a pump-probe experiment with photoelectron-photoion coincidence detection, we reconstruct the interesting excited-state spectrum from pump-probe and pump-only measurements. This approach allows us to treat background and false coincidences consistently and on the same footing. We demonstrate that the Bayesian formalism has the following advantages over simple signal subtraction: (i) the signal-to-noise ratio is significantly increased, (ii) the pump-only contribution is not overestimated, (iii) false coincidences are excluded, (iv) prior knowledge, such as positivity, is consistently incorporated, (v) confidence intervals are provided for the reconstructed spectrum, and (vi) it is applicable to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, the Bayesian approach allows us to run experiments at higher ionization rates, resulting in a significant reduction of data acquisition times. The probabilistic approach is thoroughly scrutinized by challenging mock data. The application to pump-probe coincidence measurements on acetone molecules enables quantitative interpretations
International Nuclear Information System (INIS)
Kim, Joo Yeon; Jang, Han Ki; Lee, Jai Ki
2005-01-01
Bayesian methodology is appropriated for use in PRA because subjective knowledges as well as objective data are applied to assessment. In this study, radiological risk based on Bayesian methodology is assessed for the loss of source in field radiography. The exposure scenario for the lost source presented in U.S. NRC is reconstructed by considering the domestic situation and Bayes theorem is applied to updating of failure probabilities of safety functions. In case of updating of failure probabilities, it shows that 5% Bayes credible intervals using Jeffreys prior distribution are lower than ones using vague prior distribution. It is noted that Jeffreys prior distribution is appropriated in risk assessment for systems having very low failure probabilities. And, it shows that the mean of the expected annual dose for the public based on Bayesian methodology is higher than the dose based on classical methodology because the means of the updated probabilities are higher than classical probabilities. The database for radiological risk assessment are sparse in domestic. It summarizes that Bayesian methodology can be applied as an useful alternative for risk assessment and the study on risk assessment will be contributed to risk-informed regulation in the field of radiation safety
Bayesian methods for chromosome dosimetry following a criticality accident
International Nuclear Information System (INIS)
Brame, R.S.; Groer, P.G.
2003-01-01
Radiation doses received during a criticality accident will be from a combination of fission spectrum neutrons and gamma rays. It is desirable to estimate the total dose, as well as the neutron and gamma doses. Present methods for dose estimation with chromosome aberrations after a criticality accident use point estimates of the neutron to gamma dose ratio obtained from personnel dosemeters and/or accident reconstruction calculations. In this paper a Bayesian approach to dose estimation with chromosome aberrations is developed that allows the uncertainty of the dose ratio to be considered. Posterior probability densities for the total and the neutron and gamma doses were derived. (author)
Hyvönen, Nuutti
2016-01-05
The simultaneous retrieval of the exterior boundary shape and the interior admittivity distribution of an examined body in electrical impedance tomography is considered. The reconstruction method is built for the complete electrode model and it is based on the Frechet derivative of the corresponding current-to-voltage map with respect to the body shape. The reconstruction problem is cast into the Bayesian framework, and maximum a posteriori estimates for the admittivity and the boundary geometry are computed. The feasibility of the approach is evaluated by experimental data from water tank measurements.
Mahamud, Kira; Martínez Ruiz-Funes, María José
2014-01-01
This paper describes a study dealing with the reconstruction of the lives of two Spanish primary school teachers during the Franco dictatorship (1939-1975), in order to learn to what extent such a field of research can contribute to the history of education. Two family archives provide extraordinary and unique documentation to track down their…
Brito, Angmary; Rodriguez, Maria A.; Niaz, Mansoor
2005-01-01
The objectives of this study are: (a) elaboration of a history and philosophy of science (HPS) framework based on a reconstruction of the development of the periodic table; (b) formulation of seven criteria based on the framework; and (c) evaluation of 57 freshman college-level general chemistry textbooks with respect to the presentation of the…
New method for determination of star formation history
Čeponis, Marius
2017-01-01
A New Method for Determination of Star Formation History Without stars there would not be any life and us. Almost all elements in our bodies are made in stars. Yet we still don‘t fully understand all the processes governing formation and evolution of stellar systems. Their star formation histories really help in trying to understand these processes. In this work a new Bayesian method for determination of star formation history is proposed. This method uses photometric data of resolved stars a...
Validation of Magnetic Reconstruction Codes for Real-Time Applications
International Nuclear Information System (INIS)
Mazon, D.; Murari, A.; Boulbe, C.; Faugeras, B.; Blum, J.; Svensson, J.; Quilichini, T.; Gelfusa, M.
2010-01-01
The real-time reconstruction of the plasma magnetic equilibrium in a tokamak is a key point to access high-performance regimes. Indeed, the shape of the plasma current density profile is a direct output of the reconstruction and has a leading effect for reaching a steady-state high-performance regime of operation. The challenge is thus to develop real-time methods and algorithms that reconstruct the magnetic equilibrium from the perspective of using these outputs for feedback control purposes. In this paper the validation of the JET real-time equilibrium reconstruction codes using both a Bayesian approach and a full equilibrium solver named Equinox will be detailed, the comparison being performed with the off-line equilibrium code EFIT (equilibrium fitting) or the real-time boundary reconstruction code XLOC (X-point local expansion). In this way a significant database, a methodology, and a strategy for the validation are presented. The validation of the results has been performed using a validated database of 130 JET discharges with a large variety of magnetic configurations. Internal measurements like polarimetry and motional Stark effect have been also used for the Equinox validation including some magnetohydrodynamic signatures for the assessment of the reconstructed safety profile and current density. (authors)
Bayesian soft X-ray tomography using non-stationary Gaussian Processes
International Nuclear Information System (INIS)
Li, Dong; Svensson, J.; Thomsen, H.; Werner, A.; Wolf, R.; Medina, F.
2013-01-01
In this study, a Bayesian based non-stationary Gaussian Process (GP) method for the inference of soft X-ray emissivity distribution along with its associated uncertainties has been developed. For the investigation of equilibrium condition and fast magnetohydrodynamic behaviors in nuclear fusion plasmas, it is of importance to infer, especially in the plasma center, spatially resolved soft X-ray profiles from a limited number of noisy line integral measurements. For this ill-posed inversion problem, Bayesian probability theory can provide a posterior probability distribution over all possible solutions under given model assumptions. Specifically, the use of a non-stationary GP to model the emission allows the model to adapt to the varying length scales of the underlying diffusion process. In contrast to other conventional methods, the prior regularization is realized in a probability form which enhances the capability of uncertainty analysis, in consequence, scientists who concern the reliability of their results will benefit from it. Under the assumption of normally distributed noise, the posterior distribution evaluated at a discrete number of points becomes a multivariate normal distribution whose mean and covariance are analytically available, making inversions and calculation of uncertainty fast. Additionally, the hyper-parameters embedded in the model assumption can be optimized through a Bayesian Occam's Razor formalism and thereby automatically adjust the model complexity. This method is shown to produce convincing reconstructions and good agreements with independently calculated results from the Maximum Entropy and Equilibrium-Based Iterative Tomography Algorithm methods
Bayesian soft X-ray tomography using non-stationary Gaussian Processes
Li, Dong; Svensson, J.; Thomsen, H.; Medina, F.; Werner, A.; Wolf, R.
2013-08-01
In this study, a Bayesian based non-stationary Gaussian Process (GP) method for the inference of soft X-ray emissivity distribution along with its associated uncertainties has been developed. For the investigation of equilibrium condition and fast magnetohydrodynamic behaviors in nuclear fusion plasmas, it is of importance to infer, especially in the plasma center, spatially resolved soft X-ray profiles from a limited number of noisy line integral measurements. For this ill-posed inversion problem, Bayesian probability theory can provide a posterior probability distribution over all possible solutions under given model assumptions. Specifically, the use of a non-stationary GP to model the emission allows the model to adapt to the varying length scales of the underlying diffusion process. In contrast to other conventional methods, the prior regularization is realized in a probability form which enhances the capability of uncertainty analysis, in consequence, scientists who concern the reliability of their results will benefit from it. Under the assumption of normally distributed noise, the posterior distribution evaluated at a discrete number of points becomes a multivariate normal distribution whose mean and covariance are analytically available, making inversions and calculation of uncertainty fast. Additionally, the hyper-parameters embedded in the model assumption can be optimized through a Bayesian Occam's Razor formalism and thereby automatically adjust the model complexity. This method is shown to produce convincing reconstructions and good agreements with independently calculated results from the Maximum Entropy and Equilibrium-Based Iterative Tomography Algorithm methods.
International Nuclear Information System (INIS)
Cotler, Jordan; Wilczek, Frank
2016-01-01
We introduce quantum history states and their mathematical framework, thereby reinterpreting and extending the consistent histories approach to quantum theory. Through thought experiments, we demonstrate that our formalism allows us to analyze a quantum version of history in which we reconstruct the past by observations. In particular, we can pass from measurements to inferences about ‘what happened’ in a way that is sensible and free of paradox. Our framework allows for a richer understanding of the temporal structure of quantum theory, and we construct history states that embody peculiar, non-classical correlations in time. (paper)
Image reconstruction under non-Gaussian noise
DEFF Research Database (Denmark)
Sciacchitano, Federica
During acquisition and transmission, images are often blurred and corrupted by noise. One of the fundamental tasks of image processing is to reconstruct the clean image from a degraded version. The process of recovering the original image from the data is an example of inverse problem. Due...... to the ill-posedness of the problem, the simple inversion of the degradation model does not give any good reconstructions. Therefore, to deal with the ill-posedness it is necessary to use some prior information on the solution or the model and the Bayesian approach. Additive Gaussian noise has been......D thesis intends to solve some of the many open questions for image restoration under non-Gaussian noise. The two main kinds of noise studied in this PhD project are the impulse noise and the Cauchy noise. Impulse noise is due to for instance the malfunctioning pixel elements in the camera sensors, errors...
Yuan, Ying; MacKinnon, David P.
2009-01-01
In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…
Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.
Tang, Jing; Kuwabara, Hiroto; Wong, Dean F; Rahmim, Arman
2010-08-07
We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [(11)C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.
Bayesian Approach to Spectral Function Reconstruction for Euclidean Quantum Field Theories
Burnier, Yannis; Rothkopf, Alexander
2013-11-01
We present a novel approach to the inference of spectral functions from Euclidean time correlator data that makes close contact with modern Bayesian concepts. Our method differs significantly from the maximum entropy method (MEM). A new set of axioms is postulated for the prior probability, leading to an improved expression, which is devoid of the asymptotically flat directions present in the Shanon-Jaynes entropy. Hyperparameters are integrated out explicitly, liberating us from the Gaussian approximations underlying the evidence approach of the maximum entropy method. We present a realistic test of our method in the context of the nonperturbative extraction of the heavy quark potential. Based on hard-thermal-loop correlator mock data, we establish firm requirements in the number of data points and their accuracy for a successful extraction of the potential from lattice QCD. Finally we reinvestigate quenched lattice QCD correlators from a previous study and provide an improved potential estimation at T=2.33TC.
Nilsen, T.; Divine, D.; Rypdal, M.; Werner, J.; Rypdal, K.
2016-12-01
A modified two-dimensional stochastic-diffusive energy balance model (EBM) defined on a sphere was used for generating pseudoproxy/instrumental data and target data for surface temperature. The EBM is described in Rypdal et al. (2015). The target field has prescribed long-range memory (LRM) properties in time, and a frequency-dependent autocorrelation function in space. The Bayesian hierarchical model BARCAST, was used to generate surface temperature field reconstructions of an area corresponding to the European landmass for the past millennium. BARCAST has a built-in multivariate AR(1) model for the evolution of the temperature field, with an exponential, spatial covariance function, (Tingley & Huybers, 2010). The AR(1) process has a short-range memory, and we seek to find out how the competing spatiotemporal models influence the persistence of the reconstruction. A number of pseudoproxy experiments were performed with a fixed proxy network, using different signal-to-noise ratios (SNR) and colors of noise, (white/red). To study the persistence properties, the power-law relation of the power spectral density for LRM processes was used: S(f) f-β. The spectral exponent β was estimated both for local data and the spatial mean of the full region. The local β for the target varies between (0.1, 0.4), and for the spatial mean β 0.6. Results for the reconstructions show that the local and global memory is influenced by the noise color and level. Low noise levels or absence of noise results in reconstructions that exhibit similar properties as the target, while for higher noise levels the reconstructions have memory properties of a white/red character, (SNR=0.3 by standard deviation). Since an SNR of 0.5-0.25 is considered realistic for real proxy records, this implies that estimates of temporal persistence from proxy-based reconstructions reflect the proxy noise to a high degree, and not the signal as desired. Rypdal et al., 2015: Spatiotemporal Long-Range Persistence
Chen, Yihang; Xiao, Chijie; Yang, Xiaoyi; Wang, Tianbo; Xu, Tianchao; Yu, Yi; Xu, Min; Wang, Long; Lin, Chen; Wang, Xiaogang
2017-10-01
The Laser-driven Ion beam trace probe (LITP) is a new diagnostic method for measuring poloidal magnetic field (Bp) and radial electric field (Er) in tokamaks. LITP injects a laser-driven ion beam into the tokamak, and Bp and Er profiles can be reconstructed using tomography methods. A reconstruction code has been developed to validate the LITP theory, and both 2D reconstruction of Bp and simultaneous reconstruction of Bp and Er have been attained. To reconstruct from experimental data with noise, Maximum Entropy and Gaussian-Bayesian tomography methods were applied and improved according to the characteristics of the LITP problem. With these improved methods, a reconstruction error level below 15% has been attained with a data noise level of 10%. These methods will be further tested and applied in the following LITP experiments. Supported by the ITER-CHINA program 2015GB120001, CHINA MOST under 2012YQ030142 and National Natural Science Foundation Abstract of China under 11575014 and 11375053.
Little Ice Age climate reconstruction from ensemble reanalysis of Alpine glacier fluctuations
Directory of Open Access Journals (Sweden)
M. P. Lüthi
2014-04-01
Full Text Available Mountain glaciers sample a combination of climate fields – temperature, precipitation and radiation – by accumulation and melting of ice. Flow dynamics acts as a transfer function that maps volume changes to a length response of the glacier terminus. Long histories of terminus positions have been assembled for several glaciers in the Alps. Here I analyze terminus position histories from an ensemble of seven glaciers in the Alps with a macroscopic model of glacier dynamics to derive a history of glacier equilibrium line altitude (ELA for the time span 400–2010 C.E. The resulting climatic reconstruction depends only on records of glacier variations. The reconstructed ELA history is similar to recent reconstructions of Alpine summer temperature and Atlantic Multidecadal Oscillation (AMO index, but bears little resemblance to reconstructed precipitation variations. Most reconstructed low-ELA periods coincide with large explosive volcano eruptions, hinting at a direct effect of volcanic radiative cooling on mass balance. The glacier advances during the LIA, and the retreat after 1860, can thus be mainly attributed to temperature and volcanic radiative cooling.
LensEnt2: Maximum-entropy weak lens reconstruction
Marshall, P. J.; Hobson, M. P.; Gull, S. F.; Bridle, S. L.
2013-08-01
LensEnt2 is a maximum entropy reconstructor of weak lensing mass maps. The method takes each galaxy shape as an independent estimator of the reduced shear field and incorporates an intrinsic smoothness, determined by Bayesian methods, into the reconstruction. The uncertainties from both the intrinsic distribution of galaxy shapes and galaxy shape estimation are carried through to the final mass reconstruction, and the mass within arbitrarily shaped apertures are calculated with corresponding uncertainties. The input is a galaxy ellipticity catalog with each measured galaxy shape treated as a noisy tracer of the reduced shear field, which is inferred on a fine pixel grid assuming positivity, and smoothness on scales of w arcsec where w is an input parameter. The ICF width w can be chosen by computing the evidence for it.
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Brincker, Rune
2008-01-01
Computing displacements of a structure from its measured accelerations has been major concern of some fields of engineering such as earthquake engineering. In vibration engineering also displacements are preferred to acceleration histories occasionally i.e. in the determination of forces applied...... on a structure. In brief the major problem that accompanies reconstruction of true displacement from acceleration record is the unreal drift observed in the double integrated acceleration. Purpose of the present work is to address source of the problem, introduce its treatments, show how they work and compare...
Holocene flooding history of the Lower Tagus Valley (Portugal)
Vis, G.-J.; Bohncke, S.J.P.; Schneider, H.; Kasse, C.; Coenraads-Nederveen, S.; Zuurbier, K.; Rozema, J.
2010-01-01
The present paper aims to reconstruct the Lower Tagus Valley flooding history for the last ca. 6500 a, to explore the suitability of pollen-based local vegetation development in supporting the reconstruction of flooding history, and to explain fluvial activity changes in terms of allogenic (climate,
Bayesian Inference on Gravitational Waves
Directory of Open Access Journals (Sweden)
Asad Ali
2015-12-01
Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.
Aggregate Measures of Watershed Health from Reconstructed ...
Risk-based indices such as reliability, resilience, and vulnerability (R-R-V), have the potential to serve as watershed health assessment tools. Recent research has demonstrated the applicability of such indices for water quality (WQ) constituents such as total suspended solids and nutrients on an individual basis. However, the calculations can become tedious when time-series data for several WQ constituents have to be evaluated individually. Also, comparisons between locations with different sets of constituent data can prove difficult. In this study, data reconstruction using relevance vector machine algorithm was combined with dimensionality reduction via variational Bayesian noisy principal component analysis to reconstruct and condense sparse multidimensional WQ data sets into a single time series. The methodology allows incorporation of uncertainty in both the reconstruction and dimensionality-reduction steps. The R-R-V values were calculated using the aggregate time series at multiple locations within two Indiana watersheds. Results showed that uncertainty present in the reconstructed WQ data set propagates to the aggregate time series and subsequently to the aggregate R-R-V values as well. serving as motivating examples. Locations with different WQ constituents and different standards for impairment were successfully combined to provide aggregate measures of R-R-V values. Comparisons with individual constituent R-R-V values showed that v
RECONSTRUCTING THE PHOTOMETRIC LIGHT CURVES OF EARTH AS A PLANET ALONG ITS HISTORY
International Nuclear Information System (INIS)
Sanromá, E.; Pallé, E.
2012-01-01
By utilizing satellite-based estimations of the distribution of clouds, we have studied Earth's large-scale cloudiness behavior according to latitude and surface types (ice, water, vegetation, and desert). These empirical relationships are used here to reconstruct the possible cloud distribution of historical epochs of Earth's history such as the Late Cretaceous (90 Ma ago), the Late Triassic (230 Ma ago), the Mississippian (340 Ma ago), and the Late Cambrian (500 Ma ago), when the landmass distributions were different from today's. With this information, we have been able to simulate the globally integrated photometric variability of the planet at these epochs. We find that our simple model reproduces well the observed cloud distribution and albedo variability of the modern Earth. Moreover, the model suggests that the photometric variability of the Earth was probably much larger in past epochs. This enhanced photometric variability could improve the chances for the difficult determination of the rotational period and the identification of continental landmasses for a distant planets.
Re-telling, Re-evaluating and Re-constructing
Directory of Open Access Journals (Sweden)
Gorana Tolja
2013-11-01
Full Text Available 'Graphic History: Essays on Graphic Novels and/as History '(2012 is a collection of 14 unique essays, edited by scholar Richard Iadonisi, that explores a variety of complex issues within the graphic novel medium as a means of historical narration. The essays address the issues of accuracy of re-counting history, history as re-constructed, and the ethics surrounding historical narration.
Directory of Open Access Journals (Sweden)
М. О. Подрезова
2016-12-01
Full Text Available In article, made attempt to unite separate data on history and activity of Student’s library of the Odessa I. I. Mechnikov National University. By means of newly opened archival materials, some moments from history of creation of fund of the Room for reading students in Richelieu Lyceum are reconstructed. The role of the trustee of the Odessa educational district N. I. Pirogov in creation of Student’s library and process of its further transformation in student’s department of library of the Novorossiysk University is shown. The moments of completing of fund of library by donation and purchase of books in different years of its activity are considered. Data on obtaining the books and money according to the will of the university doctor P. A. Ivanov aimed at the development of educational and auxiliary institutions of the Novorossiysk University are in detail stated.
Receiver-based recovery of clipped ofdm signals for papr reduction: A bayesian approach
Ali, Anum
2014-01-01
Clipping is one of the simplest peak-to-average power ratio reduction schemes for orthogonal frequency division multiplexing (OFDM). Deliberately clipping the transmission signal degrades system performance, and clipping mitigation is required at the receiver for information restoration. In this paper, we acknowledge the sparse nature of the clipping signal and propose a low-complexity Bayesian clipping estimation scheme. The proposed scheme utilizes a priori information about the sparsity rate and noise variance for enhanced recovery. At the same time, the proposed scheme is robust against inaccurate estimates of the clipping signal statistics. The undistorted phase property of the clipped signal, as well as the clipping likelihood, is utilized for enhanced reconstruction. Furthermore, motivated by the nature of modern OFDM-based communication systems, we extend our clipping reconstruction approach to multiple antenna receivers and multi-user OFDM.We also address the problem of channel estimation from pilots contaminated by the clipping distortion. Numerical findings are presented that depict favorable results for the proposed scheme compared to the established sparse reconstruction schemes.
Authenticating History With Oral Narratives: The Example of Ekajuk ...
African Journals Online (AJOL)
It is generally accepted that oral narratives serve as a veritable means for historical reconstruction. This holds true, particularly in societies where written documents do not subsist. The Ekajuk community, though very warlike, is a relatively small community that lacks a written history. The attempt to reconstruct the history of ...
Modelling of JET diagnostics using Bayesian Graphical Models
Energy Technology Data Exchange (ETDEWEB)
Svensson, J. [IPP Greifswald, Greifswald (Germany); Ford, O. [Imperial College, London (United Kingdom); McDonald, D.; Hole, M.; Nessi, G. von; Meakins, A.; Brix, M.; Thomsen, H.; Werner, A.; Sirinelli, A.
2011-07-01
The mapping between physics parameters (such as densities, currents, flows, temperatures etc) defining the plasma 'state' under a given model and the raw observations of each plasma diagnostic will 1) depend on the particular physics model used, 2) is inherently probabilistic, from uncertainties on both observations and instrumental aspects of the mapping, such as calibrations, instrument functions etc. A flexible and principled way of modelling such interconnected probabilistic systems is through so called Bayesian graphical models. Being an amalgam between graph theory and probability theory, Bayesian graphical models can simulate the complex interconnections between physics models and diagnostic observations from multiple heterogeneous diagnostic systems, making it relatively easy to optimally combine the observations from multiple diagnostics for joint inference on parameters of the underlying physics model, which in itself can be represented as part of the graph. At JET about 10 diagnostic systems have to date been modelled in this way, and has lead to a number of new results, including: the reconstruction of the flux surface topology and q-profiles without any specific equilibrium assumption, using information from a number of different diagnostic systems; profile inversions taking into account the uncertainties in the flux surface positions and a substantial increase in accuracy of JET electron density and temperature profiles, including improved pedestal resolution, through the joint analysis of three diagnostic systems. It is believed that the Bayesian graph approach could potentially be utilised for very large sets of diagnostics, providing a generic data analysis framework for nuclear fusion experiments, that would be able to optimally utilize the information from multiple diagnostics simultaneously, and where the explicit graph representation of the connections to underlying physics models could be used for sophisticated model testing. This
Deep phylogeny and character evolution in thecostraca (Crustacea: Maxillopoda)
DEFF Research Database (Denmark)
Pérez-Losada, Marcos; Høeg, Jens Thorvald; Crandall, Keith A.
2012-01-01
published since Darwin's seminal monographs, few studies have tested evolutionary hypotheses about Thecostraca within a phylogenetic context. In this review, we combine a Bayesian phylogenetic method and multilocus sequence data to reconstruct the evolutionary history of 12 key thecostracan phenotypic...
Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D
2013-02-01
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.
Energy Technology Data Exchange (ETDEWEB)
Stawinski, G
1998-10-26
Bayesian algorithms are developed to solve inverse problems in gamma imaging and photofission tomography. The first part of this work is devoted to the modeling of our measurement systems. Two models have been found for both applications: the first one is a simple conventional model and the second one is a cascaded point process model. EM and MCMC Bayesian algorithms for image restoration and image reconstruction have been developed for these models and compared. The cascaded point process model does not improve significantly the results previously obtained by the classical model. To original approaches have been proposed, which increase the results previously obtained. The first approach uses an inhomogeneous Markov Random Field as a prior law, and makes the regularization parameter spatially vary. However, the problem of the estimation of hyper-parameters has not been solved. In the case of the deconvolution of point sources, a second approach has been proposed, which introduces a high level prior model. The picture is modeled as a list of objects, whose parameters and number are unknown. The results obtained with this method are more accurate than those obtained with the conventional Markov Random Field prior model and require less computational costs. (author)
Evidence cross-validation and Bayesian inference of MAST plasma equilibria
Energy Technology Data Exchange (ETDEWEB)
Nessi, G. T. von; Hole, M. J. [Research School of Physical Sciences and Engineering, Australian National University, Canberra ACT 0200 (Australia); Svensson, J. [Max-Planck-Institut fuer Plasmaphysik, D-17491 Greifswald (Germany); Appel, L. [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon OX14 3DB (United Kingdom)
2012-01-15
In this paper, current profiles for plasma discharges on the mega-ampere spherical tokamak are directly calculated from pickup coil, flux loop, and motional-Stark effect observations via methods based in the statistical theory of Bayesian analysis. By representing toroidal plasma current as a series of axisymmetric current beams with rectangular cross-section and inferring the current for each one of these beams, flux-surface geometry and q-profiles are subsequently calculated by elementary application of Biot-Savart's law. The use of this plasma model in the context of Bayesian analysis was pioneered by Svensson and Werner on the joint-European tokamak [Svensson and Werner,Plasma Phys. Controlled Fusion 50(8), 085002 (2008)]. In this framework, linear forward models are used to generate diagnostic predictions, and the probability distribution for the currents in the collection of plasma beams was subsequently calculated directly via application of Bayes' formula. In this work, we introduce a new diagnostic technique to identify and remove outlier observations associated with diagnostics falling out of calibration or suffering from an unidentified malfunction. These modifications enable a good agreement between Bayesian inference of the last-closed flux-surface with other corroborating data, such as that from force balance considerations using EFIT++[Appel et al., ''A unified approach to equilibrium reconstruction'' Proceedings of the 33rd EPS Conference on Plasma Physics (Rome, Italy, 2006)]. In addition, this analysis also yields errors on the plasma current profile and flux-surface geometry as well as directly predicting the Shafranov shift of the plasma core.
Evidence cross-validation and Bayesian inference of MAST plasma equilibria
International Nuclear Information System (INIS)
Nessi, G. T. von; Hole, M. J.; Svensson, J.; Appel, L.
2012-01-01
In this paper, current profiles for plasma discharges on the mega-ampere spherical tokamak are directly calculated from pickup coil, flux loop, and motional-Stark effect observations via methods based in the statistical theory of Bayesian analysis. By representing toroidal plasma current as a series of axisymmetric current beams with rectangular cross-section and inferring the current for each one of these beams, flux-surface geometry and q-profiles are subsequently calculated by elementary application of Biot-Savart's law. The use of this plasma model in the context of Bayesian analysis was pioneered by Svensson and Werner on the joint-European tokamak [Svensson and Werner,Plasma Phys. Controlled Fusion 50(8), 085002 (2008)]. In this framework, linear forward models are used to generate diagnostic predictions, and the probability distribution for the currents in the collection of plasma beams was subsequently calculated directly via application of Bayes' formula. In this work, we introduce a new diagnostic technique to identify and remove outlier observations associated with diagnostics falling out of calibration or suffering from an unidentified malfunction. These modifications enable a good agreement between Bayesian inference of the last-closed flux-surface with other corroborating data, such as that from force balance considerations using EFIT++[Appel et al., ''A unified approach to equilibrium reconstruction'' Proceedings of the 33rd EPS Conference on Plasma Physics (Rome, Italy, 2006)]. In addition, this analysis also yields errors on the plasma current profile and flux-surface geometry as well as directly predicting the Shafranov shift of the plasma core.
Directory of Open Access Journals (Sweden)
Z. Y. Wu
2011-09-01
Full Text Available The 1951–2009 drought history of China is reconstructed using daily soil moisture values generated by the Variable Infiltration Capacity (VIC land surface macroscale hydrology model. VIC is applied over a grid of 10 458 points with a spatial resolution of 30 km × 30 km, and is driven by observed daily maximum and minimum air temperature and precipitation from 624 long-term meteorological stations. The VIC soil moisture is used to calculate the Soil Moisture Anomaly Percentage Index (SMAPI, which can be used as a measure of the severity of agricultural drought on a global basis. We have developed a SMAPI-based drought identification procedure for practical uses in the identification of both grid point and regional drought events. As a result, a total of 325 regional drought events varying in time and strength are identified from China's nine drought study regions. These drought events can thus be assessed quantitatively at different spatial and temporal scales. The result shows that the severe drought events of 1978, 2000 and 2006 are well reconstructed, which indicates that the SMAPI is capable of identifying the onset of a drought event, its progression, as well as its termination. Spatial and temporal variations of droughts in China's nine drought study regions are studied. Our result shows that on average, up to 30% of the total area of China is prone to drought. Regionally, an upward trend in drought-affected areas has been detected in three regions (Inner Mongolia, Northeast and North from 1951–2009. However, the decadal variability of droughts has been weak in the rest of the five regions (South, Southwest, East, Northwest, and Tibet. Xinjiang has even been showing steadily wetter since the 1950s. Two regional dry centres are discovered in China as the result of a combined analysis on the occurrence of drought events from both grid points and drought study regions. The first centre is located in the area partially covered by the North
Delayed breast implant reconstruction
DEFF Research Database (Denmark)
Hvilsom, Gitte B.; Hölmich, Lisbet R.; Steding-Jessen, Marianne
2012-01-01
We evaluated the association between radiation therapy and severe capsular contracture or reoperation after 717 delayed breast implant reconstruction procedures (288 1- and 429 2-stage procedures) identified in the prospective database of the Danish Registry for Plastic Surgery of the Breast during...... of radiation therapy was associated with a non-significantly increased risk of reoperation after both 1-stage (HR = 1.4; 95% CI: 0.7-2.5) and 2-stage (HR = 1.6; 95% CI: 0.9-3.1) procedures. Reconstruction failure was highest (13.2%) in the 2-stage procedures with a history of radiation therapy. Breast...... reconstruction approaches other than implants should be seriously considered among women who have received radiation therapy....
Ghosh, Sujit K
2010-01-01
Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.
Finarelli, John A; Goswami, Anjali
2013-12-01
Reconstructing evolutionary patterns and their underlying processes is a central goal in biology. Yet many analyses of deep evolutionary histories assume that data from the fossil record is too incomplete to include, and rely solely on databases of extant taxa. Excluding fossil taxa assumes that character state distributions across living taxa are faithful representations of a clade's entire evolutionary history. Many factors can make this assumption problematic. Fossil taxa do not simply lead-up to extant taxa; they represent now-extinct lineages that can substantially impact interpretations of character evolution for extant groups. Here, we analyze body mass data for extant and fossil canids (dogs, foxes, and relatives) for changes in mean and variance through time. AIC-based model selection recovered distinct models for each of eight canid subgroups. We compared model fit of parameter estimates for (1) extant data alone and (2) extant and fossil data, demonstrating that the latter performs significantly better. Moreover, extant-only analyses result in unrealistically low estimates of ancestral mass. Although fossil data are not always available, reconstructions of deep-time organismal evolution in the absence of deep-time data can be highly inaccurate, and we argue that every effort should be made to include fossil data in macroevolutionary studies. © 2013 The Authors. Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.
Albert, Jim
2009-01-01
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The earl
Teo, Thomas
2013-02-01
After suggesting that all psychologies contain indigenous qualities and discussing differences and commonalities between German and North American historiographies of psychology, an indigenous reconstruction of German critical psychology is applied. It is argued that German critical psychology can be understood as a backlash against American psychology, as a response to the Americanization of German psychology after WWII, on the background of the history of German psychology, the academic impact of the Cold War, and the trajectory of personal biographies and institutions. Using an intellectual-historical perspective, it is shown how and which indigenous dimensions played a role in the development of German critical psychology as well as the limitations to such an historical approach. Expanding from German critical psychology, the role of the critique of American psychology in various contexts around the globe is discussed in order to emphasize the relevance of indigenous historical research.
Kleibergen, F.R.; Kleijn, R.; Paap, R.
2000-01-01
We propose a novel Bayesian test under a (noninformative) Jeffreys'priorspecification. We check whether the fixed scalar value of the so-calledBayesian Score Statistic (BSS) under the null hypothesis is aplausiblerealization from its known and standardized distribution under thealternative. Unlike
Bayesian methods for proteomic biomarker development
Directory of Open Access Journals (Sweden)
Belinda Hernández
2015-12-01
In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Bayesian inference with ecological applications
Link, William A
2009-01-01
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
Xu, Yunfei; Dass, Sarat; Maiti, Tapabrata
2016-01-01
This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive di...
Diffusion archeology for diffusion progression history reconstruction
Sefer, Emre; Kingsford, Carl
2015-01-01
Diffusion through graphs can be used to model many real-world processes, such as the spread of diseases, social network memes, computer viruses, or water contaminants. Often, a real-world diffusion cannot be directly observed while it is occurring — perhaps it is not noticed until some time has passed, continuous monitoring is too costly, or privacy concerns limit data access. This leads to the need to reconstruct how the present state of the diffusion came to be from partial d...
A Bayesian framework for risk perception
van Erp, H.R.N.
2017-01-01
We present here a Bayesian framework of risk perception. This framework encompasses plausibility judgments, decision making, and question asking. Plausibility judgments are modeled by way of Bayesian probability theory, decision making is modeled by way of a Bayesian decision theory, and relevancy
Bayesian flood forecasting methods: A review
Han, Shasha; Coulibaly, Paulin
2017-08-01
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Bayesian analysis of ion beam diagnostics
International Nuclear Information System (INIS)
Toussaint, U. von; Fischer, R.; Dose, V.
2001-01-01
Ion beam diagnostics are routinely used for quantitative analysis of the surface composition of mixture materials up to a depth of a few μm. Unfortunately, advantageous properties of the diagnostics, like high depth resolution in combination with a large penetration depth, no destruction of the surface, high sensitivity for large as well as for small atomic numbers, and high sensitivity are mutually exclusive. Among other things, this is due to the ill-conditioned inverse problem of reconstructing depth distributions of the composition elements. Robust results for depth distributions are obtained with adaptive methods in the framework of Bayesian probability theory. The method of adaptive kernels allows for distributions which contain only the significant information of the data while noise fitting is avoided. This is achieved by adaptively reducing the degrees of freedom supporting the distribution. As applications for ion beam diagnostics Rutherford backscattering spectroscopy and particle induced X-ray emission are shown
[History of aesthetic rhinoplasty].
Nguyen, P S; Mazzola, R F
2014-12-01
One of the first surgical procedures described in the history of medicine is reconstructive surgery of the nose. Over the centuries, surgeons have developed techniques aimed at reconstructing noses amputated or traumatized by disease. The concept of aesthetic rhinoplasty was only introduced at the end of the 19th century. Since then, techniques have evolved toward constant ameliorations. Nowadays, this surgery is one of the most performed aesthetic procedures. Current technical sophistication is the result of over a century of history marked by many surgeons. All of these techniques derive from a detailed understanding of the anatomical nose from the surgical and artistic point of view. Copyright © 2014 Elsevier Masson SAS. All rights reserved.
Mandible reconstruction: History, state of the art and persistent problems.
Ferreira, José J; Zagalo, Carlos M; Oliveira, Marta L; Correia, André M; Reis, Ana R
2015-06-01
Mandibular reconstruction has been experiencing an amazing evolution. Several different approaches are used to reconstruct this bone and therefore have a fundamental role in the recovery of oral functions. This review aims to highlight the persistent problems associated with the approaches identified, whether bone grafts or prosthetic devices are used. A brief summary of the historical evolution of the surgical procedures is presented, as well as an insight into possible future pathways. A literature review was conducted from September to December 2012 using the PubMed database. The keyword used was "mandible reconstruction." Articles published in the last three years were included as well as the relevant references from those articles and the "historical articles" were referred. This research resulted in a monograph that this article aims to summarize. Titanium plates, bone grafts, pediculate flaps, free osteomyocutaneous flaps, rapid prototyping, and tissue engineering strategies are some of the identified possibilities. The classical approaches present considerable associated morbidity donor-site-related problems. Research that results in the development of new prosthetics devices is needed. A new prosthetic approach could minimize the identified problems and offer the patients more predictable, affordable, and comfortable solutions. This review, while affirming the evolution and the good results found with the actual approaches, emphasizes the negative aspects that still subsist. Thus, it shows that mandible reconstruction is not a closed issue. On the contrary, it remains as a research field where new findings could have a direct positive impact on patients' life quality. The identification of the persistent problems reveals the characteristics to be considered in a new prosthetic device. This could overcome the current difficulties and result in more comfortable solutions. Medical teams have the responsibility to keep patients informed about the predictable
ABCtoolbox: a versatile toolkit for approximate Bayesian computations
Directory of Open Access Journals (Sweden)
Neuenschwander Samuel
2010-03-01
Full Text Available Abstract Background The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC. It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. Conclusion ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.
Directory of Open Access Journals (Sweden)
O. N. Solomina
2013-01-01
Full Text Available Lacustrine sediments represent an important data source for glacial and palaeoclimatic reconstructions. Having a number of certain advantages, they can be successfully used as a means of specification of glacier situation and age of moraine deposits, as well as a basis for detailed climatic models of the Holocene. The article focuses on the coring of sediments of Lake Kakakel (Western Caucasus that has its goal to clarify the Holocene climatic history for the region, providing the sampling methods, lithologic description of the sediment core, obtained radiocarbon dating and the element composition of the sediments. The primary outlook over the results of coring of the sediments of the Lake Karakyol helped to reconsider the conventional opinion on the glacial fluctuations in the valley of Teberda and to assume the future possibility for high-definition palaeoclimatic reconstruction for Western Caucasus.
History Matters: What Happens When African Americans Confront Their Difficult Past.
Seitz, Phillip
2016-05-01
History and Reconstruction is an interdisciplinary project to assess the impact of African American history education for black men. Under the theory of trauma recovery, leading scholars of African American history worked with a group of ten ex-offenders, supported by the services of a psychologist and an African American cultural expert and storyteller. Results based on psychological testing and qualitative feedback showed that history can be a catalyst for personal development and transformation. It also demonstrated that difficult history can be taught and assimilated for audience benefit. History and Reconstruction was supported by the Pew Center for Arts and Heritage.
Directory of Open Access Journals (Sweden)
Brandon Lee Drake
Full Text Available Strontium isotope sourcing has become a common and useful method for assigning sources to archaeological artifacts.In Chaco Canyon, an Ancestral Pueblo regional center in New Mexico, previous studiesusing these methods have suggested that significant portion of maize and wood originate in the Chuska Mountains region, 75 km to the West [corrected]. In the present manuscript, these results were tested using both frequentist methods (to determine if geochemical sources can truly be differentiated and Bayesian methods (to address uncertainty in geochemical source attribution. It was found that Chaco Canyon and the Chuska Mountain region are not easily distinguishable based on radiogenic strontium isotope values. The strontium profiles of many geochemical sources in the region overlap, making it difficult to definitively identify any one particular geochemical source for the canyon's pre-historic maize. Bayesian mixing models support the argument that some spruce and fir wood originated in the San Mateo Mountains, but that this cannot explain all 87Sr/86Sr values in Chaco timber. Overall radiogenic strontium isotope data do not clearly identify a single major geochemical source for maize, ponderosa, and most spruce/fir timber. As such, the degree to which Chaco Canyon relied upon outside support for both food and construction material is still ambiguous.
Lattice NRQCD study on in-medium bottomonium spectra using a novel Bayesian reconstruction approach
Kim, Seyong; Petreczky, Peter; Rothkopf, Alexander
2016-01-01
We present recent results on the in-medium modification of S- and P-wave bottomonium states around the deconfinement transition. Our study uses lattice QCD with Nf = 2 + 1 light quark flavors to describe the non-perturbative thermal QCD medium between 140MeV Bayesian prescription, which provides higher accuracy than the Maximum Entropy Method. Based on a systematic comparison of interacting and free spectral functions we conclude that the ground states of both the S-wave (ϒ) and P-wave (χb1) channel survive up to T = 249MeV. Stringent upper limits on the size of the in-medium modification of bottomonium masses and widths are provided.
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.
1998-12-01
Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)
Inferring the most probable maps of underground utilities using Bayesian mapping model
Bilal, Muhammad; Khan, Wasiq; Muggleton, Jennifer; Rustighi, Emiliano; Jenks, Hugo; Pennock, Steve R.; Atkins, Phil R.; Cohn, Anthony
2018-03-01
Mapping the Underworld (MTU), a major initiative in the UK, is focused on addressing social, environmental and economic consequences raised from the inability to locate buried underground utilities (such as pipes and cables) by developing a multi-sensor mobile device. The aim of MTU device is to locate different types of buried assets in real time with the use of automated data processing techniques and statutory records. The statutory records, even though typically being inaccurate and incomplete, provide useful prior information on what is buried under the ground and where. However, the integration of information from multiple sensors (raw data) with these qualitative maps and their visualization is challenging and requires the implementation of robust machine learning/data fusion approaches. An approach for automated creation of revised maps was developed as a Bayesian Mapping model in this paper by integrating the knowledge extracted from sensors raw data and available statutory records. The combination of statutory records with the hypotheses from sensors was for initial estimation of what might be found underground and roughly where. The maps were (re)constructed using automated image segmentation techniques for hypotheses extraction and Bayesian classification techniques for segment-manhole connections. The model consisting of image segmentation algorithm and various Bayesian classification techniques (segment recognition and expectation maximization (EM) algorithm) provided robust performance on various simulated as well as real sites in terms of predicting linear/non-linear segments and constructing refined 2D/3D maps.
Book review: Bayesian analysis for population ecology
Link, William A.
2011-01-01
Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.
Gopnik, Alison; Wellman, Henry M
2012-11-01
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.
A population-based Bayesian approach to the minimal model of glucose and insulin homeostasis
DEFF Research Database (Denmark)
Andersen, Kim Emil; Højbjerre, Malene
2005-01-01
-posed estimation problem, where the reconstruction most often has been done by non-linear least squares techniques separately for each entity. The minmal model was originally specified for a single individual and does not combine several individuals with the advantage of estimating the metabolic portrait...... to a population-based model. The estimation of the parameters are efficiently implemented in a Bayesian approach where posterior inference is made through the use of Markov chain Monte Carlo techniques. Hereby we obtain a powerful and flexible modelling framework for regularizing the ill-posed estimation problem...
RIB FRACTURE AFTER BREAST RECONSTRUCTION WITH TISSUE EXPANDER
Directory of Open Access Journals (Sweden)
Uroš Ahčan
2009-08-01
Full Text Available Breast reconstruction with tissue expansion and later exchange with prosthesis is one of the most common methods for breast reconstruction. Women that are not appropriate for reconstruction with autologous tissue, women that have small breast or have a positive family history for breast cancer are most suitable for this type of reconstruction. Surgical technique of tissue expansion is relatively easy. Complications are rarely seen. With this case report we want to show the common, although occult existence of skeletal deformities in thorax after breast tissue expansion that may lead to rib fractures.
Reconstructing the past outburst history of Eta Carinae from WFPC2 proper motions
Smith, Nathan
2016-10-01
The HST archive contains multiple epochs of WFPC2 images of the nebula around Eta Carinae taken over a 15-year timespan, although only the earliest few years of data have been analyzed and published. The fact that all these images were taken with the same instrument, with the same pixel sampling and field distortion, makes them an invaluable resource for accurately measuring the expanding ejecta. The goal of a previously accepted AR proposal was to analyze the full set of appropriate continuum-filter HST images to place precise constraints on the avereage ejection date of the Homunculus Nebula; this analysis is now complete (Smith et al 2016) and the nebula appears to have been ejected in the second half of 1847. Here we propose to continue this project by constraining the motion of the more extended and much older Outer Ejecta around Eta Carinae. Older material outside the main bipolar nebula traces previous major outbursts of the star with no recorded historical observations. We propose an ambitious reduction and analysis of the complete WFPC2 imaging dataset of Eta Car. These data can reconstruct its violent mass-loss history over the past thousand years. We have already started this by analyzing two epochs of ACS F658N images, and astonishingly, these data suggested two previous eruptions in the 13th and 15th centuries assuming ballistic motion. WFPC2 images will extend the baseline by 10 yr, and critically, more than 2 epochs allow us to measure any deceleration in the ejecta. We will also analyze Doppler shifts in ground-based spectra in order to reconstruct the 3D geometry of past mass ejection. This AR proposal will fund the final year of a PhD thesis.
Brown, Joseph W; Parins-Fukuchi, Caroline; Stull, Gregory W; Vargas, Oscar M; Smith, Stephen A
2017-10-11
Puttick et al. (2017 Proc. R. Soc. B 284 , 20162290 (doi:10.1098/rspb.2016.2290)) performed a simulation study to compare accuracy among methods of inferring phylogeny from discrete morphological characters. They report that a Bayesian implementation of the Mk model (Lewis 2001 Syst. Biol. 50 , 913-925 (doi:10.1080/106351501753462876)) was most accurate (but with low resolution), while a maximum-likelihood (ML) implementation of the same model was least accurate. They conclude by strongly advocating that Bayesian implementations of the Mk model should be the default method of analysis for such data. While we appreciate the authors' attempt to investigate the accuracy of alternative methods of analysis, their conclusion is based on an inappropriate comparison of the ML point estimate, which does not consider confidence, with the Bayesian consensus, which incorporates estimation credibility into the summary tree. Using simulation, we demonstrate that ML and Bayesian estimates are concordant when confidence and credibility are comparably reflected in summary trees, a result expected from statistical theory. We therefore disagree with the conclusions of Puttick et al. and consider their prescription of any default method to be poorly founded. Instead, we recommend caution and thoughtful consideration of the model or method being applied to a morphological dataset. © 2017 The Author(s).
Jones, Matt; Love, Bradley C
2011-08-01
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls
Choosing the best ancestral character state reconstruction method.
Royer-Carenzi, Manuela; Pontarotti, Pierre; Didier, Gilles
2013-03-01
Despite its intrinsic difficulty, ancestral character state reconstruction is an essential tool for testing evolutionary hypothesis. Two major classes of approaches to this question can be distinguished: parsimony- or likelihood-based approaches. We focus here on the second class of methods, more specifically on approaches based on continuous-time Markov modeling of character evolution. Among them, we consider the most-likely-ancestor reconstruction, the posterior-probability reconstruction, the likelihood-ratio method, and the Bayesian approach. We discuss and compare the above-mentioned methods over several phylogenetic trees, adding the maximum-parsimony method performance in the comparison. Under the assumption that the character evolves according a continuous-time Markov process, we compute and compare the expectations of success of each method for a broad range of model parameter values. Moreover, we show how the knowledge of the evolution model parameters allows to compute upper bounds of reconstruction performances, which are provided as references. The results of all these reconstruction methods are quite close one to another, and the expectations of success are not so far from their theoretical upper bounds. But the performance ranking heavily depends on the topology of the studied tree, on the ancestral node that is to be inferred and on the parameter values. Consequently, we propose a protocol providing for each parameter value the best method in terms of expectation of success, with regard to the phylogenetic tree and the ancestral node to infer. Copyright © 2012 Elsevier Inc. All rights reserved.
Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography.
Bianco, V; Paturzo, M; Memmolo, P; Finizio, A; Ferraro, P; Javidi, B
2013-03-01
Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.
An Active Lattice Model in a Bayesian Framework
DEFF Research Database (Denmark)
Carstensen, Jens Michael
1996-01-01
A Markov Random Field is used as a structural model of a deformable rectangular lattice. When used as a template prior in a Bayesian framework this model is powerful for making inferences about lattice structures in images. The model assigns maximum probability to the perfect regular lattice...... by penalizing deviations in alignment and lattice node distance. The Markov random field represents prior knowledge about the lattice structure, and through an observation model that incorporates the visual appearance of the nodes, we can simulate realizations from the posterior distribution. A maximum...... a posteriori (MAP) estimate, found by simulated annealing, is used as the reconstructed lattice. The model was developed as a central part of an algorithm for automatic analylsis of genetic experiments, positioned in a lattice structure by a robot. The algorithm has been successfully applied to many images...
DEFF Research Database (Denmark)
Stahlhut, Carsten; Mørup, Morten; Winther, Ole
2011-01-01
We present an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model representation is motivated by the many random contributions to the path from sources to measurements including the tissue conductivity distribution, the geometry of the cortical s...
Proterozoic Milankovitch cycles and the history of the solar system.
Meyers, Stephen R; Malinverno, Alberto
2018-06-19
The geologic record of Milankovitch climate cycles provides a rich conceptual and temporal framework for evaluating Earth system evolution, bestowing a sharp lens through which to view our planet's history. However, the utility of these cycles for constraining the early Earth system is hindered by seemingly insurmountable uncertainties in our knowledge of solar system behavior (including Earth-Moon history), and poor temporal control for validation of cycle periods (e.g., from radioisotopic dates). Here we address these problems using a Bayesian inversion approach to quantitatively link astronomical theory with geologic observation, allowing a reconstruction of Proterozoic astronomical cycles, fundamental frequencies of the solar system, the precession constant, and the underlying geologic timescale, directly from stratigraphic data. Application of the approach to 1.4-billion-year-old rhythmites indicates a precession constant of 85.79 ± 2.72 arcsec/year (2σ), an Earth-Moon distance of 340,900 ± 2,600 km (2σ), and length of day of 18.68 ± 0.25 hours (2σ), with dominant climatic precession cycles of ∼14 ky and eccentricity cycles of ∼131 ky. The results confirm reduced tidal dissipation in the Proterozoic. A complementary analysis of Eocene rhythmites (∼55 Ma) illustrates how the approach offers a means to map out ancient solar system behavior and Earth-Moon history using the geologic archive. The method also provides robust quantitative uncertainties on the eccentricity and climatic precession periods, and derived astronomical timescales. As a consequence, the temporal resolution of ancient Earth system processes is enhanced, and our knowledge of early solar system dynamics is greatly improved.
3rd Bayesian Young Statisticians Meeting
Lanzarone, Ettore; Villalobos, Isadora; Mattei, Alessandra
2017-01-01
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).
Rise and fall of the Beringian steppe bison
DEFF Research Database (Denmark)
Shapiro, B.; Drummond, A. J.; Rambaut, A.
2004-01-01
The widespread extinctions of large mammals at the end of the Pleistocene epoch have often been attributed to the depredations of humans; here we present genetic evidence that questions this assumption. We used ancient DNA and Bayesian techniques to reconstruct a detailed genetic history of bison...
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... dynamic domains. The communication needed between instances is achieved by means of a fill-in propagation scheme....
Shah, Mihir M; Martin, Benjamin M; Stetler, Jamil L; Patel, Ankit D; Davis, S Scott; Lin, Edward; Sarmiento, Juan M
2017-09-01
Comprehensive description with illustrations of the 4 biliary reconstruction options for bile duct injury in patients with history of Roux-en-Y gastric bypass. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.
Kruschke, John K; Liddell, Torrin M
2018-02-01
In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.
CURRENT CONCEPTS IN ACL RECONSTRUCTION
Directory of Open Access Journals (Sweden)
Freddie H. Fu
2008-09-01
Full Text Available Current Concepts in ACL Reconstruction is a complete reference text composed of the most thorough collection of topics on the ACL and its surgical reconstruction compiled, with contributions from some of the world's experts and most experienced ACL surgeons. Various procedures mentioned throughout the text are also demonstrated in an accompanying video CD-ROM. PURPOSE Composing a single, comprehensive and complete information source on ACL including basic sciences, clinical issues, latest concepts and surgical techniques, from evaluation to outcome, from history to future, editors and contributors have targeted to keep the audience pace with the latest concepts and techniques for the evaluation and the treatment of ACL injuries. FEATURES The text is composed of 27 chapters in 6 sections. The first section is mostly about basic sciences, also history of the ACL, imaging, clinical approach to adolescent and pediatric patients are subjected. In the second section, Graft Choices and Arthroscopy Portals for ACL Reconstruction are mentioned. The third section is about the technique and the outcome of the single-bundle ACL reconstruction. The fourth chapter includes the techniques and outcome of the double-bundle ACL reconstruction. In the fifth chapter revision, navigation technology, rehabilitation and the evaluation of the outcome of ACL reconstruction is subjected. The sixth/the last chapter is about the future advances to reach: What We Have Learned and the Future of ACL Reconstruction. AUDIENCE Orthopedic residents, sports traumatology and knee surgery fellows, orthopedic surgeons, also scientists in basic sciences or clinicians who are studying or planning a research on ACL forms the audience group of this book. ASSESSMENT This is the latest, the most complete and comprehensive textbook of ACL reconstruction produced by the editorial work up of two pioneer and masters "Freddie H. Fu MD and Steven B. Cohen MD" with the contribution of world
Reconstructing the complex evolutionary history of mobile plasmids in red algal genomes
Lee, JunMo; Kim, Kyeong Mi; Yang, Eun Chan; Miller, Kathy Ann; Boo, Sung Min; Bhattacharya, Debashish; Yoon, Hwan Su
2016-01-01
The integration of foreign DNA into algal and plant plastid genomes is a rare event, with only a few known examples of horizontal gene transfer (HGT). Plasmids, which are well-studied drivers of HGT in prokaryotes, have been reported previously in red algae (Rhodophyta). However, the distribution of these mobile DNA elements and their sites of integration into the plastid (ptDNA), mitochondrial (mtDNA), and nuclear genomes of Rhodophyta remain unknown. Here we reconstructed the complex evolutionary history of plasmid-derived DNAs in red algae. Comparative analysis of 21 rhodophyte ptDNAs, including new genome data for 5 species, turned up 22 plasmid-derived open reading frames (ORFs) that showed syntenic and copy number variation among species, but were conserved within different individuals in three lineages. Several plasmid-derived homologs were found not only in ptDNA but also in mtDNA and in the nuclear genome of green plants, stramenopiles, and rhizarians. Phylogenetic and plasmid-derived ORF analyses showed that the majority of plasmid DNAs originated within red algae, whereas others were derived from cyanobacteria, other bacteria, and viruses. Our results elucidate the evolution of plasmid DNAs in red algae and suggest that they spread as parasitic genetic elements. This hypothesis is consistent with their sporadic distribution within Rhodophyta. PMID:27030297
Weak lensing galaxy cluster field reconstruction
Jullo, E.; Pires, S.; Jauzac, M.; Kneib, J.-P.
2014-02-01
In this paper, we compare three methods to reconstruct galaxy cluster density fields with weak lensing data. The first method called FLens integrates an inpainting concept to invert the shear field with possible gaps, and a multi-scale entropy denoising procedure to remove the noise contained in the final reconstruction, that arises mostly from the random intrinsic shape of the galaxies. The second and third methods are based on a model of the density field made of a multi-scale grid of radial basis functions. In one case, the model parameters are computed with a linear inversion involving a singular value decomposition (SVD). In the other case, the model parameters are estimated using a Bayesian Monte Carlo Markov Chain optimization implemented in the lensing software LENSTOOL. Methods are compared on simulated data with varying galaxy density fields. We pay particular attention to the errors estimated with resampling. We find the multi-scale grid model optimized with Monte Carlo Markov Chain to provide the best results, but at high computational cost, especially when considering resampling. The SVD method is much faster but yields noisy maps, although this can be mitigated with resampling. The FLens method is a good compromise with fast computation, high signal-to-noise ratio reconstruction, but lower resolution maps. All three methods are applied to the MACS J0717+3745 galaxy cluster field, and reveal the filamentary structure discovered in Jauzac et al. We conclude that sensitive priors can help to get high signal-to-noise ratio, and unbiased reconstructions.
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
Bayesian natural language semantics and pragmatics
Zeevat, Henk
2015-01-01
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.
Event Reconstruction in the PandaRoot framework
International Nuclear Information System (INIS)
Spataro, Stefano
2012-01-01
The PANDA experiment will study the collisions of beams of anti-protons, with momenta ranging from 2-15 GeV/c, with fixed proton and nuclear targets in the charm energy range, and will be built at the FAIR facility. In preparation for the experiment, the PandaRoot software framework is under development for detector simulation, reconstruction and data analysis, running on an Alien2-based grid. The basic features are handled by the FairRoot framework, based on ROOT and Virtual Monte Carlo, while the PANDA detector specifics and reconstruction code are implemented inside PandaRoot. The realization of Technical Design Reports for the tracking detectors has pushed the finalization of the tracking reconstruction code, which is complete for the Target Spectrometer, and of the analysis tools. Particle Identification algorithms are currently implemented using Bayesian approach and compared to Multivariate Analysis methods. Moreover, the PANDA data acquisition foresees a triggerless operation in which events are not defined by a hardware 1st level trigger decision, but all the signals are stored with time stamps requiring a deconvolution by the software. This has led to a redesign of the software from an event basis to a time-ordered structure. In this contribution, the reconstruction capabilities of the Panda spectrometer will be reported, focusing on the performances of the tracking system and the results for the analysis of physics benchmark channels, as well as the new (and challenging) concept of time-based simulation and its implementation.
Barthe, Stéphanie; Binelli, Giorgio; Hérault, Bruno; Scotti-Saintagne, Caroline; Sabatier, Daniel; Scotti, Ivan
2017-02-01
How Quaternary climatic and geological disturbances influenced the composition of Neotropical forests is hotly debated. Rainfall and temperature changes during and/or immediately after the last glacial maximum (LGM) are thought to have strongly affected the geographical distribution and local abundance of tree species. The paucity of the fossil records in Neotropical forests prevents a direct reconstruction of such processes. To describe community-level historical trends in forest composition, we turned therefore to inferential methods based on the reconstruction of past demographic changes. In particular, we modelled the history of rainforests in the eastern Guiana Shield over a timescale of several thousand generations, through the application of approximate Bayesian computation and maximum-likelihood methods to diversity data at nuclear and chloroplast loci in eight species or subspecies of rainforest trees. Depending on the species and on the method applied, we detected population contraction, expansion or stability, with a general trend in favour of stability or expansion, with changes presumably having occurred during or after the LGM. These findings suggest that Guiana Shield rainforests have globally persisted, while expanding, through the Quaternary, but that different species have experienced different demographic events, with a trend towards the increase in frequency of light-demanding, disturbance-associated species. © 2016 John Wiley & Sons Ltd.
Bayesian methods in reliability
Sander, P.; Badoux, R.
1991-11-01
The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.
Bayesian networks and food security - An introduction
Stein, A.
2004-01-01
This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
High migration rates shape the postglacial history of amphi-Atlantic bryophytes.
Désamoré, Aurélie; Patiño, Jairo; Mardulyn, Patrick; Mcdaniel, Stuart F; Zanatta, Florian; Laenen, Benjamin; Vanderpoorten, Alain
2016-11-01
Paleontological evidence and current patterns of angiosperm species richness suggest that European biota experienced more severe bottlenecks than North American ones during the last glacial maximum. How well this pattern fits other plant species is less clear. Bryophytes offer a unique opportunity to contrast the impact of the last glacial maximum in North America and Europe because about 60% of the European bryoflora is shared with North America. Here, we use population genetic analyses based on approximate Bayesian computation on eight amphi-Atlantic species to test the hypothesis that North American populations were less impacted by the last glacial maximum, exhibiting higher levels of genetic diversity than European ones and ultimately serving as a refugium for the postglacial recolonization of Europe. In contrast with this hypothesis, the best-fit demographic model involved similar patterns of population size contractions, comparable levels of genetic diversity and balanced migration rates between European and North American populations. Our results thus suggest that bryophytes have experienced comparable demographic glacial histories on both sides of the Atlantic. Although a weak, but significant genetic structure was systematically recovered between European and North American populations, evidence for migration from and towards both continents suggests that amphi-Atlantic bryophyte population may function as a metapopulation network. Reconstructing the biogeographic history of either North American or European bryophyte populations therefore requires a large, trans-Atlantic geographic framework. © 2016 John Wiley & Sons Ltd.
Kernel Bayesian ART and ARTMAP.
Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan
2018-02-01
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.
Bayesian networks improve causal environmental ...
Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value
Bayesian Latent Class Analysis Tutorial.
Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca
2018-01-01
This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.
CSIR Research Space (South Africa)
Rosman, Benjamin
2016-02-01
Full Text Available Keywords Policy Reuse · Reinforcement Learning · Online Learning · Online Bandits · Transfer Learning · Bayesian Optimisation · Bayesian Decision Theory. 1 Introduction As robots and software agents are becoming more ubiquitous in many applications.... The agent has access to a library of policies (pi1, pi2 and pi3), and has previously experienced a set of task instances (τ1, τ2, τ3, τ4), as well as samples of the utilities of the library policies on these instances (the black dots indicate the means...
2010-05-01
operationnelle pour la reconstruction de sources, au systeme de model- isation urbaine multi echelle integre mis en oeuvre dans !’infrastructure informatique...or urbanLS, respectively. However, for the Bayesian inversion of concentration data to be practical , fast and efficient techniques are required for...sensitivity and/or uncertainty analysis methods that have been used to quantify and reduce them. In this paper , all the various error contributions to
Bai, Bing
2012-03-01
There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.
Directory of Open Access Journals (Sweden)
Simon Boitard
2016-03-01
Full Text Available Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey, PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.
Bayesian models: A statistical primer for ecologists
Hobbs, N. Thompson; Hooten, Mevin B.
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models
Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W; Kinnersley, Nelson; Heilmann, Cory R; Ohlssen, David; Rochester, George
2014-01-01
Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability. Copyright © 2013 John Wiley & Sons, Ltd.
Model Selection in Historical Research Using Approximate Bayesian Computation
Rubio-Campillo, Xavier
2016-01-01
Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. Case Study This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester’s laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Impact Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence. PMID:26730953
Bayesian Alternation During Tactile Augmentation
Directory of Open Access Journals (Sweden)
Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
An introduction to Bayesian statistics in health psychology.
Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske
2017-09-01
The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.
Zhang, W Q; Zhang, M H
2013-09-03
Many mitochondrial DNA sequences are used to estimate phylogenetic relationships among animal taxa and perform molecular phylogenetic evolution analysis. With the continuous development of sequencing technology, numerous mitochondrial sequences have been released in public databases, especially complete mitochondrial DNA sequences. Using multiple sequences is better than using single sequences for phylogenetic analysis of animals because multiple sequences have sufficient information for evolutionary process reconstruction. Therefore, we performed phylogenetic analyses of 14 species of Felidae based on complete mitochondrial genome sequences, with Canis familiaris as an outgroup, using neighbor joining, maximum likelihood, maximum parsimony, and Bayesian inference methods. The consensus phylogenetic trees supported the monophyly of Felidae, and the family could be divided into 2 subfamilies, Felinae and Pantherinae. The genus Panthera and species tigris were also studied in detail. Meanwhile, the divergence of this family was estimated by phylogenetic analysis using the Bayesian method with a relaxed molecular clock, and the results shown were consistent with previous studies. In summary, the evolution of Felidae was reconstructed by phylogenetic analysis based on mitochondrial genome sequences. The described method may be broadly applicable for phylogenetic analyses of anima taxa.
Bayesian Network Induction via Local Neighborhoods
National Research Council Canada - National Science Library
Margaritis, Dimitris
1999-01-01
.... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...
Climate reconstruction by regression - 32 variations on a theme
Energy Technology Data Exchange (ETDEWEB)
Buerger, Gerd; Fast, Irina; Cubasch, Ulrich [FU Berlin (Germany). Inst. fuer Meteorologie
2006-02-15
Regression-based methods fail to provide a sufficiently unique reconstruction of a given millennial history of Northern Hemisphere mean temperature. They instead offer a multitude of variants, depending on the specific data processing scheme. Using a simulated climate history with noise-disturbed pseudo-proxies, we systematically test a set of such configurations, each of which appears to be a priori reasonable, with existing applications elsewhere. This results in an entire spectrum between practically useless and almost perfect reconstructions. The reason lies in the fact that the training variations are not representative of the full millennium, and the regression equations have to be extrapolated. This creates an error that is proportional to both the model uncertainty and the proxy amplitudes. Estimation of that uncertainty is paramount for a useful millennial reconstruction, especially if it is of the parameter-loaded multiproxy type.
Maeda, Shin-ichi
2014-01-01
Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.
Bayesian Data Analysis (lecture 2)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Bayesian Data Analysis (lecture 1)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Learning Local Components to Understand Large Bayesian Networks
DEFF Research Database (Denmark)
Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge
2009-01-01
(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....
Philosophy and the practice of Bayesian statistics.
Gelman, Andrew; Shalizi, Cosma Rohilla
2013-02-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.
Reconstructing phylogenetic networks using maximum parsimony.
Nakhleh, Luay; Jin, Guohua; Zhao, Fengmei; Mellor-Crummey, John
2005-01-01
Phylogenies - the evolutionary histories of groups of organisms - are one of the most widely used tools throughout the life sciences, as well as objects of research within systematics, evolutionary biology, epidemiology, etc. Almost every tool devised to date to reconstruct phylogenies produces trees; yet it is widely understood and accepted that trees oversimplify the evolutionary histories of many groups of organims, most prominently bacteria (because of horizontal gene transfer) and plants (because of hybrid speciation). Various methods and criteria have been introduced for phylogenetic tree reconstruction. Parsimony is one of the most widely used and studied criteria, and various accurate and efficient heuristics for reconstructing trees based on parsimony have been devised. Jotun Hein suggested a straightforward extension of the parsimony criterion to phylogenetic networks. In this paper we formalize this concept, and provide the first experimental study of the quality of parsimony as a criterion for constructing and evaluating phylogenetic networks. Our results show that, when extended to phylogenetic networks, the parsimony criterion produces promising results. In a great majority of the cases in our experiments, the parsimony criterion accurately predicts the numbers and placements of non-tree events.
'n Histories-opvoedkundige rekonstruksie van die geletterdheid van ...
African Journals Online (AJOL)
Areas of main emphases in international history of education research on literacy are identified, and the research apparatus available for the reconstruction of the literacy history of South Africa is surveyed. This is followed by mapping the outlines of the history of the literacy of South Africa's population. In conclusion, and in ...
A practical exact maximum compatibility algorithm for reconstruction of recent evolutionary history
Cherry, Joshua L.
2017-01-01
Background Maximum compatibility is a method of phylogenetic reconstruction that is seldom applied to molecular sequences. It may be ideal for certain applications, such as reconstructing phylogenies of closely-related bacteria on the basis of whole-genome sequencing. Results Here I present an algorithm that rapidly computes phylogenies according to a compatibility criterion. Although based on solutions to the maximum clique problem, this algorithm deals properly with ambiguities in the data....
Reconstruction from gamma radiography and ultrasonic images
International Nuclear Information System (INIS)
Gautier, S.; Lavayssiere, B.; Idier, J.; Mohammad-Djafari, A.
1998-02-01
This work deals with the three-dimensional reconstruction from gamma radiographic and ultrasonic images. Such an issue belongs to the field of data fusion since the data provide complementary information. The two sets of data are independently related to two sets of parameters: gamma ray attenuation and ultrasonic reflectivity. The fusion problem is addressed in a Bayesian framework; the kingpin of the task is then to define a joint a priori model for both attenuation and reflectivity. Thus, the developing of this model and the entailed joint estimation constitute the principal contribution of this work. The results of real data treatments demonstrate the validity of this method as compared to a sequential approach of the two sets of data
DEFF Research Database (Denmark)
Christensen, Nana Louise; Tolbod, Lars Poulsen
PET scans. 3) Static and dynamic images from a set of 7 patients (BSA: 1.6-2.2 m2) referred for 82Rb cardiac PET was analyzed using a range of beta factors. Results were compared to the institution’s standard clinical practice reconstruction protocol. All scans were performed on GE DMI Digital......Aim: Q.Clear reconstruction is expected to improve detection of perfusion defects in cardiac PET due to the high degree of image convergence and effective noise suppression. However, 82Rb (T½=76s) possess a special problem, since count statistics vary significantly not only between patients...... statistics using a cardiac PET phantom as well as a selection of clinical patients referred for 82Rb cardiac PET. Methods: The study consistent of 3 parts: 1) A thorax-cardiac phantom was scanned for 10 minutes after injection of 1110 MBq 82Rb. Frames at 3 different times after infusion were reconstructed...
Bayesian analysis of the astrobiological implications of life's early emergence on Earth.
Spiegel, David S; Turner, Edwin L
2012-01-10
Life arose on Earth sometime in the first few hundred million years after the young planet had cooled to the point that it could support water-based organisms on its surface. The early emergence of life on Earth has been taken as evidence that the probability of abiogenesis is high, if starting from young Earth-like conditions. We revisit this argument quantitatively in a bayesian statistical framework. By constructing a simple model of the probability of abiogenesis, we calculate a bayesian estimate of its posterior probability, given the data that life emerged fairly early in Earth's history and that, billions of years later, curious creatures noted this fact and considered its implications. We find that, given only this very limited empirical information, the choice of bayesian prior for the abiogenesis probability parameter has a dominant influence on the computed posterior probability. Although terrestrial life's early emergence provides evidence that life might be abundant in the universe if early-Earth-like conditions are common, the evidence is inconclusive and indeed is consistent with an arbitrarily low intrinsic probability of abiogenesis for plausible uninformative priors. Finding a single case of life arising independently of our lineage (on Earth, elsewhere in the solar system, or on an extrasolar planet) would provide much stronger evidence that abiogenesis is not extremely rare in the universe.
ZHOU, Lin
1996-01-01
In this paper I consider social choices under uncertainty. I prove that any social choice rule that satisfies independence of irrelevant alternatives, translation invariance, and weak anonymity is consistent with ex post Bayesian utilitarianism
Spectral image reconstruction using an edge preserving spatio-spectral Wiener estimation.
Urban, Philipp; Rosen, Mitchell R; Berns, Roy S
2009-08-01
Reconstruction of spectral images from camera responses is investigated using an edge preserving spatio-spectral Wiener estimation. A Wiener denoising filter and a spectral reconstruction Wiener filter are combined into a single spatio-spectral filter using local propagation of the noise covariance matrix. To preserve edges the local mean and covariance matrix of camera responses is estimated by bilateral weighting of neighboring pixels. We derive the edge-preserving spatio-spectral Wiener estimation by means of Bayesian inference and show that it fades into the standard Wiener reflectance estimation shifted by a constant reflectance in case of vanishing noise. Simulation experiments conducted on a six-channel camera system and on multispectral test images show the performance of the filter, especially for edge regions. A test implementation of the method is provided as a MATLAB script at the first author's website.
Learning Bayesian networks for discrete data
Liang, Faming; Zhang, Jian
2009-01-01
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly
Searching Algorithm Using Bayesian Updates
Caudle, Kyle
2010-01-01
In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…
Bayesian estimates of linkage disequilibrium
Directory of Open Access Journals (Sweden)
Abad-Grau María M
2007-06-01
Full Text Available Abstract Background The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. Results This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNPs that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. Conclusion Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.
["Long-branch Attraction" artifact in phylogenetic reconstruction].
Li, Yi-Wei; Yu, Li; Zhang, Ya-Ping
2007-06-01
Phylogenetic reconstruction among various organisms not only helps understand their evolutionary history but also reveal several fundamental evolutionary questions. Understanding of the evolutionary relationships among organisms establishes the foundation for the investigations of other biological disciplines. However, almost all the widely used phylogenetic methods have limitations which fail to eliminate systematic errors effectively, preventing the reconstruction of true organismal relationships. "Long-branch Attraction" (LBA) artifact is one of the most disturbing factors in phylogenetic reconstruction. In this review, the conception and analytic method as well as the avoidance strategy of LBA were summarized. In addition, several typical examples were provided. The approach to avoid and resolve LBA artifact has been discussed.
Reconstruction of fire history of the Yukon-Kuskokwim Delta, Alaska
Sae-lim, J.; Mann, P. J.; Russell, J. M.; Natali, S.; Vachula, R. S.; Schade, J. D.; Holmes, R. M.
2017-12-01
Wildfire is an important disturbance in Arctic ecosystems and can cause abrupt perturbations in global carbon cycling and atmospheric chemistry. Over the next few decades, arctic fire frequency, intensity and extent is projected to increase due to anthropogenic climate change, as regional air temperatures are increasing at more than twice the global average. In order to more accurately predict the anthropogenic impacts of climate change on tundra fire regimes, it is critical to have detailed knowledge of the natural frequency and extent of past wildfires. However, reliable historical records only extend back a few hundred years, whereas climatic shifts have affected fire regimes for thousands of years. In this work we analyzed a lake sediment core collected from the Yukon-Kuskokwim (YK) Delta, Alaska, a region that has recently experienced fire and is predicted to see increasing fire frequency in the near future. Our primary lake site is situated adjacent to recent burned areas, providing a `calibration' point and also attesting to the sensitivity of the sites. We used charcoal counts alongside geochemical measurements (C:N, 13C, 15N, 210Pb, X-ray fluorescence analyses of elemental chemistry) to reconstruct past fire history and ecosystem responses during the late Holocene. Average C (%C) and N concentrations (%N) in the core were 8.10 ±0.74% and 0.48 ±0.05%, resulting in C:N ratios of 16.80 ±0.74. The values are generally stable, with no obvious trend in C, N or C:N with depth; however, fluctuations in sediment composition in other nearby lake sediment cores possibly suggests shifts in lake conditions (oxic, anoxic) over time that might result from paleofires. This study provides a baseline for estimated fire return intervals in the YK Delta that will support more accurate projections of tundra fire frequencies under a changing climate.
DEFF Research Database (Denmark)
Catana, Leo
2016-01-01
In a series of articles from the 1980s and 1990s, Michael Frede analysed the history of histories of philosophy written over the last three hundred years. According to Frede, modern scholars have degenerated into what he calls a “doxographical” mode of writing the history of philosophy. Instead, he...... argued, these scholars should write what he called “philosophical” history of philosophy, first established in the last decades of the seventeenth century but since abandoned. In the present article it is argued that Frede’s reconstruction of the history of histories of philosophy is historically...
Learning Bayesian networks for discrete data
Liang, Faming
2009-02-01
Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.
A default Bayesian hypothesis test for ANOVA designs
Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.
2012-01-01
This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA
Olivares, Ela I; Lage-Castellanos, Agustín; Bobes, María A; Iglesias, Jaime
2018-01-01
We investigated the neural correlates of the access to and retrieval of face structure information in contrast to those concerning the access to and retrieval of person-related verbal information, triggered by faces. We experimentally induced stimulus familiarity via a systematic learning procedure including faces with and without associated verbal information. Then, we recorded event-related potentials (ERPs) in both intra-domain (face-feature) and cross-domain (face-occupation) matching tasks while N400-like responses were elicited by incorrect eyes-eyebrows completions and occupations, respectively. A novel Bayesian source reconstruction approach plus conjunction analysis of group effects revealed that in both cases the generated N170s were of similar amplitude but had different neural origin. Thus, whereas the N170 of faces was associated predominantly to right fusiform and occipital regions (the so-called "Fusiform Face Area", "FFA" and "Occipital Face Area", "OFA", respectively), the N170 of occupations was associated to a bilateral very posterior activity, suggestive of basic perceptual processes. Importantly, the right-sided perceptual P200 and the face-related N250 were evoked exclusively in the intra-domain task, with sources in OFA and extensively in the fusiform region, respectively. Regarding later latencies, the intra-domain N400 seemed to be generated in right posterior brain regions encompassing mainly OFA and, to some extent, the FFA, likely reflecting neural operations triggered by structural incongruities. In turn, the cross-domain N400 was related to more anterior left-sided fusiform and temporal inferior sources, paralleling those described previously for the classic verbal N400. These results support the existence of differentiated neural streams for face structure and person-related verbal processing triggered by faces, which can be activated differentially according to specific task demands.
Directory of Open Access Journals (Sweden)
Ela I. Olivares
2018-03-01
Full Text Available We investigated the neural correlates of the access to and retrieval of face structure information in contrast to those concerning the access to and retrieval of person-related verbal information, triggered by faces. We experimentally induced stimulus familiarity via a systematic learning procedure including faces with and without associated verbal information. Then, we recorded event-related potentials (ERPs in both intra-domain (face-feature and cross-domain (face-occupation matching tasks while N400-like responses were elicited by incorrect eyes-eyebrows completions and occupations, respectively. A novel Bayesian source reconstruction approach plus conjunction analysis of group effects revealed that in both cases the generated N170s were of similar amplitude but had different neural origin. Thus, whereas the N170 of faces was associated predominantly to right fusiform and occipital regions (the so-called “Fusiform Face Area”, “FFA” and “Occipital Face Area”, “OFA”, respectively, the N170 of occupations was associated to a bilateral very posterior activity, suggestive of basic perceptual processes. Importantly, the right-sided perceptual P200 and the face-related N250 were evoked exclusively in the intra-domain task, with sources in OFA and extensively in the fusiform region, respectively. Regarding later latencies, the intra-domain N400 seemed to be generated in right posterior brain regions encompassing mainly OFA and, to some extent, the FFA, likely reflecting neural operations triggered by structural incongruities. In turn, the cross-domain N400 was related to more anterior left-sided fusiform and temporal inferior sources, paralleling those described previously for the classic verbal N400. These results support the existence of differentiated neural streams for face structure and person-related verbal processing triggered by faces, which can be activated differentially according to specific task demands.
Task-based data-acquisition optimization for sparse image reconstruction systems
Chen, Yujia; Lou, Yang; Kupinski, Matthew A.; Anastasio, Mark A.
2017-03-01
Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.
Bayesian Networks An Introduction
Koski, Timo
2009-01-01
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni
A default Bayesian hypothesis test for mediation.
Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan
2015-03-01
In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).
Maximum entropy based reconstruction of soft X ray emissivity profiles in W7-AS
International Nuclear Information System (INIS)
Ertl, K.; Linden, W. von der; Dose, V.; Weller, A.
1996-01-01
The reconstruction of 2-D emissivity profiles from soft X ray tomography measurements constitutes a highly underdetermined and ill-posed inversion problem, because of the restricted viewing access, the number of chords and the increased noise level in most plasma devices. An unbiased and consistent probabilistic approach within the framework of Bayesian inference is provided by the maximum entropy method, which is independent of model assumptions, but allows any prior knowledge available to be incorporated. The formalism is applied to the reconstruction of emissivity profiles in an NBI heated plasma discharge to determine the dependence of the Shafranov shift on β, the reduction of which was a particular objective in designing the advanced W7-AS stellarator. (author). 40 refs, 7 figs
A Bayesian model for binary Markov chains
Directory of Open Access Journals (Sweden)
Belkheir Essebbar
2004-02-01
Full Text Available This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Directory of Open Access Journals (Sweden)
Hiroshi eSaito
2014-03-01
Full Text Available The decision making behaviors of humans and animals adapt and then satisfy an ``operant matching law'' in certain type of tasks. This was first pointed out by Herrnstein in his foraging experiments on pigeons. The matching law has been one landmark for elucidating the underlying processes of decision making and its learning in the brain. An interesting question is whether decisions are made deterministically or probabilistically. Conventional learning models of the matching law are based on the latter idea; they assume that subjects learn choice probabilities of respective alternatives and decide stochastically with the probabilities. However, it is unknown whether the matching law can be accounted for by a deterministic strategy or not. To answer this question, we propose several deterministic Bayesian decision making models that have certain incorrect beliefs about an environment. We claim that a simple model produces behavior satisfying the matching law in static settings of a foraging task but not in dynamic settings. We found that the model that has a belief that the environment is volatile works well in the dynamic foraging task and exhibits undermatching, which is a slight deviation from the matching law observed in many experiments. This model also demonstrates the double-exponential reward history dependency of a choice and a heavier-tailed run-length distribution, as has recently been reported in experiments on monkeys.
Universal Darwinism As a Process of Bayesian Inference.
Campbell, John O
2016-01-01
Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.
Smoothing expansion rate data to reconstruct cosmological matter perturbations
Energy Technology Data Exchange (ETDEWEB)
Gonzalez, J.E.; Alcaniz, J.S.; Carvalho, J.C., E-mail: javierernesto@on.br, E-mail: alcaniz@on.br, E-mail: jcarvalho@on.br [Departamento de Astronomia, Observatório Nacional, Rua Gal. José Cristino, 77, Rio de Janeiro, RJ 20921-400 (Brazil)
2017-08-01
The existing degeneracy between different dark energy and modified gravity cosmologies at the background level may be broken by analyzing quantities at the perturbative level. In this work, we apply a non-parametric smoothing (NPS) method to reconstruct the expansion history of the Universe ( H ( z )) from model-independent cosmic chronometers and high- z quasar data. Assuming a homogeneous and isotropic flat universe and general relativity (GR) as the gravity theory, we calculate the non-relativistic matter perturbations in the linear regime using the H ( z ) reconstruction and realistic values of Ω {sub m} {sub 0} and σ{sub 8} from Planck and WMAP-9 collaborations. We find a good agreement between the measurements of the growth rate and f σ{sub 8}( z ) from current large-scale structure observations and the estimates obtained from the reconstruction of the cosmic expansion history. Considering a recently proposed null test for GR using matter perturbations, we also apply the NPS method to reconstruct f σ{sub 8}( z ). For this case, we find a ∼ 3σ tension (good agreement) with the standard relativistic cosmology when the Planck (WMAP-9) priors are used.
Smoothing expansion rate data to reconstruct cosmological matter perturbations
International Nuclear Information System (INIS)
Gonzalez, J.E.; Alcaniz, J.S.; Carvalho, J.C.
2017-01-01
The existing degeneracy between different dark energy and modified gravity cosmologies at the background level may be broken by analyzing quantities at the perturbative level. In this work, we apply a non-parametric smoothing (NPS) method to reconstruct the expansion history of the Universe ( H ( z )) from model-independent cosmic chronometers and high- z quasar data. Assuming a homogeneous and isotropic flat universe and general relativity (GR) as the gravity theory, we calculate the non-relativistic matter perturbations in the linear regime using the H ( z ) reconstruction and realistic values of Ω m 0 and σ 8 from Planck and WMAP-9 collaborations. We find a good agreement between the measurements of the growth rate and f σ 8 ( z ) from current large-scale structure observations and the estimates obtained from the reconstruction of the cosmic expansion history. Considering a recently proposed null test for GR using matter perturbations, we also apply the NPS method to reconstruct f σ 8 ( z ). For this case, we find a ∼ 3σ tension (good agreement) with the standard relativistic cosmology when the Planck (WMAP-9) priors are used.
Multilocus dataset reveals demographic histories of two peat mosses in Europe
Directory of Open Access Journals (Sweden)
Hock Zsófia
2007-08-01
Full Text Available Abstract Background Revealing the past and present demographic history of populations is of high importance to evaluate the conservation status of species. Demographic data can be obtained by direct monitoring or by analysing data of historical and recent collections. Although these methods provide the most detailed information they are very time consuming. Another alternative way is to make use of the information accumulated in the species' DNA over its history. Recent development of the coalescent theory makes it possible to reconstruct the demographic history of species using nucleotide polymorphism data. To separate the effect of natural selection and demography, multilocus analysis is needed because these two forces can produce similar patterns of polymorphisms. In this study we investigated the amount and pattern of sequence variability of a Europe wide sample set of two peat moss species (Sphagnum fimbriatum and S. squarrosum with similar distributions and mating systems but presumably contrasting historical demographies using 3 regions of the nuclear genome (appr. 3000 bps. We aimed to draw inferences concerning demographic, and phylogeographic histories of the species. Results All three nuclear regions supported the presence of an Atlantic and Non-Atlantic clade of S. fimbriatum suggesting glacial survival of the species along the Atlantic coast of Europe. Contrarily, S. squarrosum haplotypes showed three clades but no geographic structure at all. Maximum likelihood, mismatch and Bayesian analyses supported a severe historical bottleneck and a relatively recent demographic expansion of the Non-Atlantic clade of S. fimbriatum, whereas size of S. squarrosum populations has probably decreased in the past. Species wide molecular diversity of the two species was nearly the same with an excess of replacement mutations in S. fimbriatum. Similar levels of molecular diversity, contrasting phylogeographic patterns and excess of replacement
Daniel Goodman’s empirical approach to Bayesian statistics
Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina
2016-01-01
Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.
Approximation methods for efficient learning of Bayesian networks
Riggelsen, C
2008-01-01
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.
2016-02-13
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver
2016-01-01
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Implementing the Bayesian paradigm in risk analysis
International Nuclear Information System (INIS)
Aven, T.; Kvaloey, J.T.
2002-01-01
The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas
Pollegioni, Paola; Woeste, Keith; Chiocchini, Francesca; Del Lungo, Stefano; Ciolfi, Marco; Olimpieri, Irene; Tortolano, Virginia; Clark, Jo; Hemery, Gabriel E; Mapelli, Sergio; Malvolti, Maria Emilia
2017-01-01
Common walnut (Juglans regia L) is an economically important species cultivated worldwide for its high-quality wood and nuts. It is generally accepted that after the last glaciation J. regia survived and grew in almost completely isolated stands in Asia, and that ancient humans dispersed walnuts across Asia and into new habitats via trade and cultural expansion. The history of walnut in Europe is a matter of debate, however. In this study, we estimated the genetic diversity and structure of 91 Eurasian walnut populations using 14 neutral microsatellites. By integrating fossil pollen, cultural, and historical data with population genetics, and approximate Bayesian analysis, we reconstructed the demographic history of walnut and its routes of dispersal across Europe. The genetic data confirmed the presence of walnut in glacial refugia in the Balkans and western Europe. We conclude that human-mediated admixture between Anatolian and Balkan walnut germplasm started in the Early Bronze Age, and between western Europe and the Balkans in eastern Europe during the Roman Empire. A population size expansion and subsequent decline in northeastern and western Europe was detected in the last five centuries. The actual distribution of walnut in Europe resulted from the combined effects of expansion/contraction from multiple refugia after the Last Glacial Maximum and its human exploitation over the last 5,000 years.
Energy Technology Data Exchange (ETDEWEB)
Mueller, Rachel Lockridge; Macey, J. Robert; Jaekel, Martin; Wake, David B.; Boore, Jeffrey L.
2004-08-01
The evolutionary history of the largest salamander family (Plethodontidae) is characterized by extreme morphological homoplasy. Analysis of the mechanisms generating such homoplasy requires an independent, molecular phylogeny. To this end, we sequenced 24 complete mitochondrial genomes (22 plethodontids and two outgroup taxa), added data for three species from GenBank, and performed partitioned and unpartitioned Bayesian, ML, and MP phylogenetic analyses. We explored four dataset partitioning strategies to account for evolutionary process heterogeneity among genes and codon positions, all of which yielded increased model likelihoods and decreased numbers of supported nodes in the topologies (PP > 0.95) relative to the unpartitioned analysis. Our phylogenetic analyses yielded congruent trees that contrast with the traditional morphology-based taxonomy; the monophyly of three out of four major groups is rejected. Reanalysis of current hypotheses in light of these new evolutionary relationships suggests that (1) a larval life history stage re-evolved from a direct-developing ancestor multiple times, (2) there is no phylogenetic support for the ''Out of Appalachia'' hypothesis of plethodontid origins, and (3) novel scenarios must be reconstructed for the convergent evolution of projectile tongues, reduction in toe number, and specialization for defensive tail loss. Some of these novel scenarios imply morphological transformation series that proceed in the opposite direction than was previously thought. In addition, they suggest surprising evolutionary lability in traits previously interpreted to be conservative.
BREAST RECONSTRUCTIONS AFTER BREAST CANCER TREATING
Directory of Open Access Journals (Sweden)
Erik Vrabič
2018-02-01
Full Text Available Background. Breasts are an important symbol of physical beauty, feminity, mothering and sexual desire through the entire history of mankind. Lost of the whole or part of the breast is functional and aesthetic disturbance for woman. It is understandable, that the woman, who is concerned over breast loss, is as appropriate as another person´s concern over the loss of a limb or other body part. Before the 1960, breast reconstruction was considered as a dangerous procedure and it was almost prohibited. Considering the psychological importance of the breast in modern society, the possibility of breast reconstruction for the woman about to undergo a mastectomy is a comforting alternative. We can perform breast reconstruction with autologous tissue (autologous reconstruction, with breast implants and combination of both methods. For autologous reconstruction we can use local tissue (local flaps, or tissue from distant parts of the body (free vascular tissue transfer. Tissue expansion must be performed first, in many cases of breast reconstructions with breast implants. Conclusions. Possibility of breast reconstruction made a big progress last 3 decades. Today we are able to reconstruct almost every defect of the breast and the entire breast. Breast reconstruction rise the quality of life for breast cancer patients. Breast reconstruction is a team work of experts from many medicine specialites. In Slovenia we can offer breast reconstruction for breast cancer patients in Ljubljana, where plastic surgeons from Clinical Department for Plastic Surgery and Burns cooperate with oncologic surgeons. Ten years ago a similar cooperation between plastic surgeons and surgeons of the Centre for Breast Diseases was established in Maribor.
Interactive Instruction in Bayesian Inference
DEFF Research Database (Denmark)
Khan, Azam; Breslav, Simon; Hornbæk, Kasper
2018-01-01
An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....
RECONSTRUCTING THE SOLAR WIND FROM ITS EARLY HISTORY TO CURRENT EPOCH
Energy Technology Data Exchange (ETDEWEB)
Airapetian, Vladimir S.; Usmanov, Arcadi V., E-mail: vladimir.airapetian@nasa.gov, E-mail: avusmanov@gmail.com [NASA Goddard Space Flight Center, Greenbelt, MD (United States)
2016-02-01
Stellar winds from active solar-type stars can play a crucial role in removal of stellar angular momentum and erosion of planetary atmospheres. However, major wind properties except for mass-loss rates cannot be directly derived from observations. We employed a three-dimensional magnetohydrodynamic Alfvén wave driven solar wind model, ALF3D, to reconstruct the solar wind parameters including the mass-loss rate, terminal velocity, and wind temperature at 0.7, 2, and 4.65 Gyr. Our model treats the wind thermal electrons, protons, and pickup protons as separate fluids and incorporates turbulence transport, eddy viscosity, turbulent resistivity, and turbulent heating to properly describe proton and electron temperatures of the solar wind. To study the evolution of the solar wind, we specified three input model parameters, the plasma density, Alfvén wave amplitude, and the strength of the dipole magnetic field at the wind base for each of three solar wind evolution models that are consistent with observational constrains. Our model results show that the velocity of the paleo solar wind was twice as fast, ∼50 times denser and 2 times hotter at 1 AU in the Sun's early history at 0.7 Gyr. The theoretical calculations of mass-loss rate appear to be in agreement with the empirically derived values for stars of various ages. These results can provide realistic constraints for wind dynamic pressures on magnetospheres of (exo)planets around the young Sun and other active stars, which is crucial in realistic assessment of the Joule heating of their ionospheres and corresponding effects of atmospheric erosion.
RECONSTRUCTING THE SOLAR WIND FROM ITS EARLY HISTORY TO CURRENT EPOCH
International Nuclear Information System (INIS)
Airapetian, Vladimir S.; Usmanov, Arcadi V.
2016-01-01
Stellar winds from active solar-type stars can play a crucial role in removal of stellar angular momentum and erosion of planetary atmospheres. However, major wind properties except for mass-loss rates cannot be directly derived from observations. We employed a three-dimensional magnetohydrodynamic Alfvén wave driven solar wind model, ALF3D, to reconstruct the solar wind parameters including the mass-loss rate, terminal velocity, and wind temperature at 0.7, 2, and 4.65 Gyr. Our model treats the wind thermal electrons, protons, and pickup protons as separate fluids and incorporates turbulence transport, eddy viscosity, turbulent resistivity, and turbulent heating to properly describe proton and electron temperatures of the solar wind. To study the evolution of the solar wind, we specified three input model parameters, the plasma density, Alfvén wave amplitude, and the strength of the dipole magnetic field at the wind base for each of three solar wind evolution models that are consistent with observational constrains. Our model results show that the velocity of the paleo solar wind was twice as fast, ∼50 times denser and 2 times hotter at 1 AU in the Sun's early history at 0.7 Gyr. The theoretical calculations of mass-loss rate appear to be in agreement with the empirically derived values for stars of various ages. These results can provide realistic constraints for wind dynamic pressures on magnetospheres of (exo)planets around the young Sun and other active stars, which is crucial in realistic assessment of the Joule heating of their ionospheres and corresponding effects of atmospheric erosion
Universal Darwinism as a process of Bayesian inference
Directory of Open Access Journals (Sweden)
John Oberon Campbell
2016-06-01
Full Text Available Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians. As Bayesian inference can always be cast in terms of (variational free energy minimization, natural selection can be viewed as comprising two components: a generative model of an ‘experiment’ in the external world environment, and the results of that 'experiment' or the 'surprise' entailed by predicted and actual outcomes of the ‘experiment’. Minimization of free energy implies that the implicit measure of 'surprise' experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.
Bayesian analysis of magnetic island dynamics
International Nuclear Information System (INIS)
Preuss, R.; Maraschek, M.; Zohm, H.; Dose, V.
2003-01-01
We examine a first order differential equation with respect to time used to describe magnetic islands in magnetically confined plasmas. The free parameters of this equation are obtained by employing Bayesian probability theory. Additionally, a typical Bayesian change point is solved in the process of obtaining the data
Bayesian Recovery of Clipped OFDM Signals: A Receiver-based Approach
Al-Rabah, Abdullatif R.
2013-05-01
Recently, orthogonal frequency-division multiplexing (OFDM) has been adopted for high-speed wireless communications due to its robustness against multipath fading. However, one of the main fundamental drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Several techniques have been proposed for PAPR reduction. Most of these techniques require transmitter-based (pre-compensated) processing. On the other hand, receiver-based alternatives would save the power and reduce the transmitter complexity. By keeping this in mind, a possible approach is to limit the amplitude of the OFDM signal to a predetermined threshold and equivalently a sparse clipping signal is added. Then, estimating this clipping signal at the receiver to recover the original signal. In this work, we propose a Bayesian receiver-based low-complexity clipping signal recovery method for PAPR reduction. The method is able to i) effectively reduce the PAPR via simple clipping scheme at the transmitter side, ii) use Bayesian recovery algorithm to reconstruct the clipping signal at the receiver side by measuring part of subcarriers, iii) perform well in the absence of statistical information about the signal (e.g. clipping level) and the noise (e.g. noise variance), and at the same time iv is energy efficient due to its low complexity. Specifically, the proposed recovery technique is implemented in data-aided based. The data-aided method collects clipping information by measuring reliable data subcarriers, thus makes full use of spectrum for data transmission without the need for tone reservation. The study is extended further to discuss how to improve the recovery of the clipping signal utilizing some features of practical OFDM systems i.e., the oversampling and the presence of multiple receivers. Simulation results demonstrate the superiority of the proposed technique over other recovery algorithms. The overall objective is to show that the receiver-based Bayesian technique is highly
Museum DNA reveals the demographic history of the endangered Seychelles warbler.
Spurgin, Lewis G; Wright, David J; van der Velde, Marco; Collar, Nigel J; Komdeur, Jan; Burke, Terry; Richardson, David S
2014-11-01
The importance of evolutionary conservation - how understanding evolutionary forces can help guide conservation decisions - is widely recognized. However, the historical demography of many endangered species is unknown, despite the fact that this can have important implications for contemporary ecological processes and for extinction risk. Here, we reconstruct the population history of the Seychelles warbler (Acrocephalus sechellensis) - an ecological model species. By the 1960s, this species was on the brink of extinction, but its previous history is unknown. We used DNA samples from contemporary and museum specimens spanning 140 years to reconstruct bottleneck history. We found a 25% reduction in genetic diversity between museum and contemporary populations, and strong genetic structure. Simulations indicate that the Seychelles warbler was bottlenecked from a large population, with an ancestral N e of several thousands falling to Seychelles warbler, and our results will inform conservation practices. Reconstructing the population history of this species also allows us to better understand patterns of genetic diversity, inbreeding and promiscuity in the contemporary populations. Our approaches can be applied across species to test ecological hypotheses and inform conservation.
Bayesian ensemble refinement by replica simulations and reweighting
Hummer, Gerhard; Köfinger, Jürgen
2015-12-01
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.
A 60-yr record of atmospheric carbon monoxide reconstructed from Greenland firn air
Petrenko, V. V.; Martinerie, P.; Novelli, P.; Etheridge, D. M.; Levin, I.; Wang, Z.; Blunier, T.; Chappellaz, J.; Kaiser, J.; Lang, P.; Steele, L. P.; Hammer, S.; Mak, J.; Langenfelds, R. L.; Schwander, J.; Severinghaus, J. P.; Witrant, E.; Petron, G.; Battle, M. O.; Forster, G.; Sturges, W. T.; Lamarque, J.-F.; Steffen, K.; White, J. W. C.
2012-08-01
We present a reconstruction of the Northern Hemisphere (NH) high latitude atmospheric carbon monoxide (CO) mole fraction from Greenland firn air. Firn air samples were collected at three deep ice core sites in Greenland (NGRIP in 2001, Summit in 2006 and NEEM in 2008). CO records from the three sites agree well with each other as well as with recent atmospheric measurements, indicating that CO is well preserved in the firn at these sites. CO atmospheric history was reconstructed back to the year 1950 from the measurements using a combination of two forward models of gas transport in firn and an inverse model. The reconstructed history suggests that Arctic CO was already higher in 1950 than it is today. CO mole fractions rose gradually until the 1970s and peaked in the 1970s or early 1980s, followed by a decline to today's levels. We compare the CO history with the atmospheric histories of methane, light hydrocarbons, molecular hydrogen, CO stable isotopes and hydroxyl radical (OH), as well as with published CO emission inventories and results of a historical run from a chemistry-transport model. We find that the reconstructed Greenland CO history cannot be reconciled with available emission inventories unless large changes in OH are assumed. We argue that the available CO emission inventories chronically underestimate NH emissions, and fail to capture the emission decline starting in the late 1970s, which was most likely due to reduced emissions from road transportation in North America and Europe.
Bayesian Decision Theoretical Framework for Clustering
Chen, Mo
2011-01-01
In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…
Lattice NRQCD study on in-medium bottomonium spectra using a novel Bayesian reconstruction approach
International Nuclear Information System (INIS)
Kim, Seyong; Petreczky, Peter; Rothkopf, Alexander
2016-01-01
We present recent results on the in-medium modification of S- and P-wave bottomonium states around the deconfinement transition. Our study uses lattice QCD with N f = 2 + 1 light quark flavors to describe the non-perturbative thermal QCD medium between 140MeV < T < 249MeV and deploys lattice regularized non-relativistic QCD (NRQCD) effective field theory to capture the physics of heavy quark bound states immersed therein. The spectral functions of the 3 S 1 (ϒ) and 3 P 1 (χ b1 ) bottomonium states are extracted from Euclidean time Monte Carlo simulations using a novel Bayesian prescription, which provides higher accuracy than the Maximum Entropy Method. Based on a systematic comparison of interacting and free spectral functions we conclude that the ground states of both the S-wave (ϒ) and P-wave (χ b1 ) channel survive up to T = 249MeV. Stringent upper limits on the size of the in-medium modification of bottomonium masses and widths are provided
Directory of Open Access Journals (Sweden)
KS Dhillon
2014-11-01
Full Text Available We are all aware that there has been a dramatic increase in the number of anterior cruciate ligament (ACL reconstructions that are carried out here in Malaysia as well as around the world. The numbers of ACL injuries have undoubtedly increased over the years with greater participation of young adults in sporting activities. However it is not certain whether the increase in the numbers of reconstructions can be accounted for by the increasing numbers of ACL injuries. Without doubt commercial interests as well the influence of the biomedical companies have a role to play. In the past the rationale for surgical treatment of an ACL tear was that the ACL is vital for knee function and that in the long term ACL deficiency will lead to more injuries of the meniscus and more degeneration of the joint. This belief was prevalent because the natural history of an ACL deficient knee and the ultimate outcome of reconstruction of the ACL were both not known. However in recent years a substantial amount of research has been published, which has elucidated the natural history of ACL deficient knees as well as the long term outcome of reconstruction of the ACL.
Quantum-Like Representation of Non-Bayesian Inference
Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.
2013-01-01
This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.
Correct Bayesian and frequentist intervals are similar
International Nuclear Information System (INIS)
Atwood, C.L.
1986-01-01
This paper argues that Bayesians and frequentists will normally reach numerically similar conclusions, when dealing with vague data or sparse data. It is shown that both statistical methodologies can deal reasonably with vague data. With sparse data, in many important practical cases Bayesian interval estimates and frequentist confidence intervals are approximately equal, although with discrete data the frequentist intervals are somewhat longer. This is not to say that the two methodologies are equally easy to use: The construction of a frequentist confidence interval may require new theoretical development. Bayesians methods typically require numerical integration, perhaps over many variables. Also, Bayesian can easily fall into the trap of over-optimism about their amount of prior knowledge. But in cases where both intervals are found correctly, the two intervals are usually not very different. (orig.)
Using consensus bayesian network to model the reactive oxygen species regulatory pathway.
Directory of Open Access Journals (Sweden)
Liangdong Hu
Full Text Available Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.
Bayesian analysis of rare events
Energy Technology Data Exchange (ETDEWEB)
Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
THz-SAR Vibrating Target Imaging via the Bayesian Method
Directory of Open Access Journals (Sweden)
Bin Deng
2017-01-01
Full Text Available Target vibration bears important information for target recognition, and terahertz, due to significant micro-Doppler effects, has strong advantages for remotely sensing vibrations. In this paper, the imaging characteristics of vibrating targets with THz-SAR are at first analyzed. An improved algorithm based on an excellent Bayesian approach, that is, the expansion-compression variance-component (ExCoV method, has been proposed for reconstructing scattering coefficients of vibrating targets, which provides more robust and efficient initialization and overcomes the deficiencies of sidelobes as well as artifacts arising from the traditional correlation method. A real vibration measurement experiment of idle cars was performed to validate the range model. Simulated SAR data of vibrating targets and a tank model in a real background in 220 GHz show good performance at low SNR. Rapidly evolving high-power terahertz devices will offer viable THz-SAR application at a distance of several kilometers.
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
Rodrigues, João Fabrício Mota; Diniz-Filho, José Alexandre Felizola
2016-08-01
Habitat may be viewed as an important life history component potentially related to diversification patterns. However, differences in diversification rates between aquatic and terrestrial realms are still poorly explored. Testudines is a group distributed worldwide that lives in aquatic and terrestrial environments, but until now no-one has evaluated the diversification history of the group as a whole. We aim here to investigate the diversification history of turtles and to test if habitat influenced speciation rate in these animals. We reconstructed the phylogeny of the modern species of chelonians and estimated node divergence dates using molecular markers and a Bayesian approach. Then, we used Bayesian Analyses of Macroevolutionary Mixtures to evaluate the diversification history of turtles and evaluate the effect of habitat on this pattern. Our reconstructed phylogeny covered 300 species (87% of the total diversity of the group). We found that the emydid subfamily Deirochelyinae, which forms the turtle hotspot in south-eastern United States, had an increase in its speciation rate, and that Galapagos tortoises had similar increases. Current speciation rates are lower in terrestrial turtles, contradicting studies supporting the idea terrestrial animals diversify more than aquatic species. Our results suggest that habitat, ecological opportunities, island invasions, and climatic factors are important drivers of diversification in modern turtles and reinforce the importance of habitat as a diversification driver. Copyright © 2016 Elsevier Inc. All rights reserved.
Reconstruction of dynamical systems from interspike intervals
International Nuclear Information System (INIS)
Sauer, T.
1994-01-01
Attractor reconstruction from interspike interval (ISI) data is described, in rough analogy with Taken's theorem for attractor reconstruction from time series. Assuming a generic integrate-and-fire model coupling the dynamical system to the spike train, there is a one-to-one correspondence between the system states and interspike interval vectors of sufficiently large dimension. The correspondence has an important implication: interspike intervals can be forecast from past history. We show that deterministically driven ISI series can be distinguished from stochastically driven ISI series on the basis of prediction error
Robust Bayesian detection of unmodelled bursts
International Nuclear Information System (INIS)
Searle, Antony C; Sutton, Patrick J; Tinto, Massimo; Woan, Graham
2008-01-01
We develop a Bayesian treatment of the problem of detecting unmodelled gravitational wave bursts using the new global network of interferometric detectors. We also compare this Bayesian treatment with existing coherent methods, and demonstrate that the existing methods make implicit assumptions on the distribution of signals that make them sub-optimal for realistic signal populations
BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES
Energy Technology Data Exchange (ETDEWEB)
Iliadis, C.; Anderson, K. S. [Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Coc, A. [Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), CNRS/IN2P3, Univ. Paris-Sud, Université Paris–Saclay, Bâtiment 104, F-91405 Orsay Campus (France); Timmes, F. X.; Starrfield, S., E-mail: iliadis@unc.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1504 (United States)
2016-11-01
The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.
Directory of Open Access Journals (Sweden)
Lívia A. de Carvalho Mondin
2018-01-01
Full Text Available Pleistocene climate changes were major historical events that impacted South American biodiversity. Although the effects of such changes are well-documented for several biomes, it is poorly known how these climate shifts affected the biodiversity of the Pantanal floodplain. Fish are one of the most diverse groups in the Pantanal floodplains and can be taken as a suitable biological model for reconstructing paleoenvironmental scenarios. To identify the effects of Pleistocene climate changes on Pantanal’s ichthyofauna, we used genetic data from multiple populations of a top-predator long-distance migratory fish, Salminus brasiliensis. We specifically investigated whether Pleistocene climate changes affected the demography of this species. If this was the case, we expected to find changes in population size over time. Thus, we assessed the genetic diversity of S. brasiliensis to trace the demographic history of nine populations from the Upper Paraguay basin, which includes the Pantanal floodplain, that form a single genetic group, employing approximate Bayesian computation (ABC to test five scenarios: constant population, old expansion, old decline, old bottleneck following by recent expansion, and old expansion following by recent decline. Based on two mitochondrial DNA markers, our inferences from ABC analysis, the results of Bayesian skyline plot, the implications of star-like networks, and the patterns of genetic diversity (high haplotype diversity and low-to-moderate nucleotide diversity indicated a sudden population expansion. ABC allowed us to make strong quantitative inferences about the demographic history of S. brasiliensis. We estimated a small ancestral population size that underwent a drastic fivefold expansion, probably associated with the colonization of newly formed habitats. The estimated time of this expansion was consistent with a humid and warm phase as inferred by speleothem growth phases and travertine records during
de Oliveira Bünger, Mariana; Fernanda Mazine, Fiorella; Forest, Félix; Leandro Bueno, Marcelo; Renato Stehmann, João; Lucas, Eve J
2016-12-01
Eugenia sect. Phyllocalyx Nied. includes 14 species endemic to the Neotropics, mostly distributed in the Atlantic coastal forests of Brazil. Here the first comprehensive phylogenetic study of this group is presented, and this phylogeny is used as the basis to evaluate the recent infrageneric classification in Eugenia sensu lato (s.l.) to test the history of the evolution of traits in the group and test hypotheses associated with the history of this clade. A total of 42 taxa were sampled, of which 14 were Eugenia sect. Phyllocalyx for one nuclear (ribosomal internal transcribed spacer) and four plastid markers (psbA-trnH, rpl16, trnL-rpl32 and trnQ-rps16). The relationships were reconstructed based on Bayesian analysis and maximum likelihood. Additionally, ancestral area analysis and modelling methods were used to estimate species dispersal, comparing historically climatic stable (refuges) and unstable areas. Maximum likelihood and Bayesian inferences indicate that Eugenia sect. Phyllocalyx is paraphyletic and the two clades recovered are characterized by combinations of morphological characters. Phylogenetic relationships support a link between Cerrado and south-eastern species and a difference in the composition of species from north-eastern and south-eastern Atlantic forest. Refugia and stable areas identified within unstable areas suggest that these areas were important to maintain diversity in the Atlantic forest biodiversity hotspot. This study provides a robust phylogenetic framework to address important historical questions for Eugenia s.l. within an evolutionary context, supporting the need for better taxonomic study of one of the largest genera in the Neotropics. Furthermore, valuable insight is offered into diversification and biome shifts of plant species in the highly environmentally impacted Atlantic forest of South America. Evidence is presented that climate stability in the south-eastern Atlantic forest during the Quaternary contributed to the
Can a significance test be genuinely Bayesian?
Pereira, Carlos A. de B.; Stern, Julio Michael; Wechsler, Sergio
2008-01-01
The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.
Bayesian image restoration, using configurations
Thorarinsdottir, Thordis
2006-01-01
In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan
2004-01-01
We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...
Bayesian analysis of CCDM models
Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
Bayesian analysis of CCDM models
Energy Technology Data Exchange (ETDEWEB)
Jesus, J.F. [Universidade Estadual Paulista (Unesp), Câmpus Experimental de Itapeva, Rua Geraldo Alckmin 519, Vila N. Sra. de Fátima, Itapeva, SP, 18409-010 Brazil (Brazil); Valentim, R. [Departamento de Física, Instituto de Ciências Ambientais, Químicas e Farmacêuticas—ICAQF, Universidade Federal de São Paulo (UNIFESP), Unidade José Alencar, Rua São Nicolau No. 210, Diadema, SP, 09913-030 Brazil (Brazil); Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk [Institute of Cosmology and Gravitation—University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX United Kingdom (United Kingdom)
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
2016-01-05
SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function
Reconstructing plate-motion changes in the presence of finite-rotations noise.
Iaffaldano, Giampiero; Bodin, Thomas; Sambridge, Malcolm
2012-01-01
Understanding lithospheric plate motions is of paramount importance to geodynamicists. Much effort is going into kinematic reconstructions featuring progressively finer temporal resolution. However, the challenge of precisely identifying ocean-floor magnetic lineations, and uncertainties in geomagnetic reversal timescales result in substantial finite-rotations noise. Unless some type of temporal smoothing is applied, the scenario arising at the native temporal resolution is puzzling, as plate motions vary erratically and significantly over short periods (<1 Myr). This undermines our ability to make geodynamic inferences, as the rates at which forces need to be built upon plates to explain these kinematics far exceed the most optimistic estimates. Here we show that the largest kinematic changes reconstructed across the Atlantic, Indian and South Pacific ridges arise from data noise. We overcome this limitation using a trans-dimensional hierarchical Bayesian framework. We find that plate-motion changes occur on timescales no shorter than a few million years, yielding simpler kinematic patterns and more plausible dynamics.
Tomasello, Salvatore; Álvarez, Inés; Vargas, Pablo; Oberprieler, Christoph
2015-01-01
The present study provides results of multi-species coalescent species tree analyses of DNA sequences sampled from multiple nuclear and plastid regions to infer the phylogenetic relationships among the members of the subtribe Leucanthemopsidinae (Compositae, Anthemideae), to which besides the annual Castrilanthemum debeauxii (Degen, Hervier & É.Rev.) Vogt & Oberp., one of the rarest flowering plant species of the Iberian Peninsula, two other unispecific genera (Hymenostemma, Prolongoa), and the polyploidy complex of the genus Leucanthemopsis belong. Based on sequence information from two single- to low-copy nuclear regions (C16, D35, characterised by Chapman et al. (2007)), the multi-copy region of the nrDNA internal transcribed spacer regions ITS1 and ITS2, and two intergenic spacer regions of the cpDNA gene trees were reconstructed using Bayesian inference methods. For the reconstruction of a multi-locus species tree we applied three different methods: (a) analysis of concatenated sequences using Bayesian inference (MrBayes), (b) a tree reconciliation approach by minimizing the number of deep coalescences (PhyloNet), and (c) a coalescent-based species-tree method in a Bayesian framework ((∗)BEAST). All three species tree reconstruction methods unequivocally support the close relationship of the subtribe with the hitherto unclassified genus Phalacrocarpum, the sister-group relationship of Castrilanthemum with the three remaining genera of the subtribe, and the further sister-group relationship of the clade of Hymenostemma+Prolongoa with a monophyletic genus Leucanthemopsis. Dating of the (∗)BEAST phylogeny supports the long-lasting (Early Miocene, 15-22Ma) taxonomical independence and the switch from the plesiomorphic perennial to the apomorphic annual life-form assumed for the Castrilanthemum lineage that may have occurred not earlier than in the Pliocene (3Ma) when the establishment of a Mediterranean climate with summer droughts triggered evolution towards
Life histories in occupational therapy clinical practice.
Frank, G
1996-04-01
This article defines and compares several narrative methods used to describe and interpret patients' lives. The biographical methods presented are case histories, life-charts, life histories, life stories, assisted autobiography, hermeneutic case reconstruction, therapeutic employment, volitional narratives, and occupational storytelling and story making. Emphasis is placed the clinician as a collaborator and interpreter of the patient's life through ongoing interactions and dialogue.
Bayesian Inference for Functional Dynamics Exploring in fMRI Data
Directory of Open Access Journals (Sweden)
Xuan Guo
2016-01-01
Full Text Available This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM, Bayesian Connectivity Change Point Model (BCCPM, and Dynamic Bayesian Variable Partition Model (DBVPM, and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.
Reconstructing galaxy histories from globular clusters.
West, Michael J; Côté, Patrick; Marzke, Ronald O; Jordán, Andrés
2004-01-01
Nearly a century after the true nature of galaxies as distant 'island universes' was established, their origin and evolution remain great unsolved problems of modern astrophysics. One of the most promising ways to investigate galaxy formation is to study the ubiquitous globular star clusters that surround most galaxies. Globular clusters are compact groups of up to a few million stars. They generally formed early in the history of the Universe, but have survived the interactions and mergers that alter substantially their parent galaxies. Recent advances in our understanding of the globular cluster systems of the Milky Way and other galaxies point to a complex picture of galaxy genesis driven by cannibalism, collisions, bursts of star formation and other tumultuous events.
Bayesian inference in mass flow simulations - from back calculation to prediction
Kofler, Andreas; Fischer, Jan-Thomas; Hellweger, Valentin; Huber, Andreas; Mergili, Martin; Pudasaini, Shiva; Fellin, Wolfgang; Oberguggenberger, Michael
2017-04-01
Mass flow simulations are an integral part of hazard assessment. Determining the hazard potential requires a multidisciplinary approach, including different scientific fields such as geomorphology, meteorology, physics, civil engineering and mathematics. An important task in snow avalanche simulation is to predict process intensities (runout, flow velocity and depth, ...). The application of probabilistic methods allows one to develop a comprehensive simulation concept, ranging from back to forward calculation and finally to prediction of mass flow events. In this context optimized parameter sets for the used simulation model or intensities of the modeled mass flow process (e.g. runout distances) are represented by probability distributions. Existing deterministic flow models, in particular with respect to snow avalanche dynamics, contain several parameters (e.g. friction). Some of these parameters are more conceptual than physical and their direct measurement in the field is hardly possible. Hence, parameters have to be optimized by matching simulation results to field observations. This inverse problem can be solved by a Bayesian approach (Markov chain Monte Carlo). The optimization process yields parameter distributions, that can be utilized for probabilistic reconstruction and prediction of avalanche events. Arising challenges include the limited amount of observations, correlations appearing in model parameters or observed avalanche characteristics (e.g. velocity and runout) and the accurate handling of ensemble simulations, always taking into account the related uncertainties. Here we present an operational Bayesian simulation framework with r.avaflow, the open source GIS simulation model for granular avalanches and debris flows.
A Bayesian approach to spectral quantitative photoacoustic tomography
International Nuclear Information System (INIS)
Pulkkinen, A; Kaipio, J P; Tarvainen, T; Cox, B T; Arridge, S R
2014-01-01
A Bayesian approach to the optical reconstruction problem associated with spectral quantitative photoacoustic tomography is presented. The approach is derived for commonly used spectral tissue models of optical absorption and scattering: the absorption is described as a weighted sum of absorption spectra of known chromophores (spatially dependent chromophore concentrations), while the scattering is described using Mie scattering theory, with the proportionality constant and spectral power law parameter both spatially-dependent. It is validated using two-dimensional test problems composed of three biologically relevant chromophores: fat, oxygenated blood and deoxygenated blood. Using this approach it is possible to estimate the Grüneisen parameter, the absolute chromophore concentrations, and the Mie scattering parameters associated with spectral photoacoustic tomography problems. In addition, the direct estimation of the spectral parameters is compared to estimates obtained by fitting the spectral parameters to estimates of absorption, scattering and Grüneisen parameter at the investigated wavelengths. It is shown with numerical examples that the direct estimation results in better accuracy of the estimated parameters. (papers)
Particle identification in ALICE: a Bayesian approach
Adam, J.; Adamova, D.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossu, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortes Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Denes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Corchero, M. A. Diaz; Dietel, T.; Dillenseger, P.; Divia, R.; Djuvsland, O.; Dobrin, A.; Gimenez, D. Domenicis; Doenigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, A. S.; Gonzalez, V.; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, O.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbaer, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Bustamante, R. T. Jimenez; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kaplin, V.; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, M. Mohisin; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, J. S.; Kim, M.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein-Boesing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Meethaleveedu, G. Koyithatta; Kralik, I.; Kravcakova, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kucera, V.; Kuijer, P. G.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; Monzon, I. Leon; Leon Vargas, H.; Leoncino, M.; Levai, P.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; Torres, E. Lopez; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mares, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marin, A.; Markert, C.; Marquard, M.; Martin, N. A.; Blanco, J. Martin; Martinengo, P.; Martinez, M. I.; Garcia, G. Martinez; Pedreira, M. Martinez; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Perez, J. Mercado; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montano Zetina, L.; Montes, E.; De Godoy, D. A. Moreira; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paic, G.; Pal, S. K.; Pan, J.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Da Costa, H. Pereira; Peresunko, D.; Lara, C. E. Perez; Lezama, E. Perez; Peskov, V.; Pestov, Y.; Petracek, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Raesaenen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodriguez Cahuantzi, M.; Manso, A. Rodriguez; Roed, K.; Rogochaya, E.; Rohr, D.; Roehrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Montero, A. J. Rubio; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Safarik, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Sefcik, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; de Souza, R. D.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Sumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Munoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thaeder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Palomo, L. Valencia; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vyvre, P. Vande; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limon, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Baillie, O. Villalobos; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Voelkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrlakova, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Watanabe, D.; Watanabe, Y.; Weiser, D. F.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yano, S.; Yasin, Z.; Yokoyama, H.; Yoo, I. -K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Zavada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, C.; Zhao, C.; Zhigareva, N.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.
2016-01-01
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
2006-01-01
We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...
A 60 yr record of atmospheric carbon monoxide reconstructed from Greenland firn air
Petrenko, V. V.; Martinerie, P.; Novelli, P.; Etheridge, D. M.; Levin, I.; Wang, Z.; Blunier, T.; Chappellaz, J.; Kaiser, J.; Lang, P.; Steele, L. P.; Hammer, S.; Mak, J.; Langenfelds, R. L.; Schwander, J.; Severinghaus, J. P.; Witrant, E.; Petron, G.; Battle, M. O.; Forster, G.; Sturges, W. T.; Lamarque, J.-F.; Steffen, K.; White, J. W. C.
2013-08-01
We present the first reconstruction of the Northern Hemisphere (NH) high latitude atmospheric carbon monoxide (CO) mole fraction from Greenland firn air. Firn air samples were collected at three deep ice core sites in Greenland (NGRIP in 2001, Summit in 2006 and NEEM in 2008). CO records from the three sites agree well with each other as well as with recent atmospheric measurements, indicating that CO is well preserved in the firn at these sites. CO atmospheric history was reconstructed back to the year 1950 from the measurements using a combination of two forward models of gas transport in firn and an inverse model. The reconstructed history suggests that Arctic CO in 1950 was 140-150 nmol mol-1, which is higher than today's values. CO mole fractions rose by 10-15 nmol mol-1 from 1950 to the 1970s and peaked in the 1970s or early 1980s, followed by a ≈ 30 nmol mol-1 decline to today's levels. We compare the CO history with the atmospheric histories of methane, light hydrocarbons, molecular hydrogen, CO stable isotopes and hydroxyl radicals (OH), as well as with published CO emission inventories and results of a historical run from a chemistry-transport model. We find that the reconstructed Greenland CO history cannot be reconciled with available emission inventories unless unrealistically large changes in OH are assumed. We argue that the available CO emission inventories strongly underestimate historical NH emissions, and fail to capture the emission decline starting in the late 1970s, which was most likely due to reduced emissions from road transportation in North America and Europe.
A 60 yr record of atmospheric carbon monoxide reconstructed from Greenland firn air
Directory of Open Access Journals (Sweden)
V. V. Petrenko
2013-08-01
Full Text Available We present the first reconstruction of the Northern Hemisphere (NH high latitude atmospheric carbon monoxide (CO mole fraction from Greenland firn air. Firn air samples were collected at three deep ice core sites in Greenland (NGRIP in 2001, Summit in 2006 and NEEM in 2008. CO records from the three sites agree well with each other as well as with recent atmospheric measurements, indicating that CO is well preserved in the firn at these sites. CO atmospheric history was reconstructed back to the year 1950 from the measurements using a combination of two forward models of gas transport in firn and an inverse model. The reconstructed history suggests that Arctic CO in 1950 was 140–150 nmol mol−1, which is higher than today's values. CO mole fractions rose by 10–15 nmol mol−1 from 1950 to the 1970s and peaked in the 1970s or early 1980s, followed by a ≈ 30 nmol mol−1 decline to today's levels. We compare the CO history with the atmospheric histories of methane, light hydrocarbons, molecular hydrogen, CO stable isotopes and hydroxyl radicals (OH, as well as with published CO emission inventories and results of a historical run from a chemistry-transport model. We find that the reconstructed Greenland CO history cannot be reconciled with available emission inventories unless unrealistically large changes in OH are assumed. We argue that the available CO emission inventories strongly underestimate historical NH emissions, and fail to capture the emission decline starting in the late 1970s, which was most likely due to reduced emissions from road transportation in North America and Europe.
A Bayesian Justification for Random Sampling in Sample Survey
Directory of Open Access Journals (Sweden)
Glen Meeden
2012-07-01
Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.
Hierarchical Bayesian Modeling of Fluid-Induced Seismicity
Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.
2017-11-01
In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.
Mishra, Arabinda; Anderson, Adam W; Wu, Xi; Gore, John C; Ding, Zhaohua
2010-08-01
The purpose of this work is to design a neuronal fiber tracking algorithm, which will be more suitable for reconstruction of fibers associated with functionally important regions in the human brain. The functional activations in the brain normally occur in the gray matter regions. Hence the fibers bordering these regions are weakly myelinated, resulting in poor performance of conventional tractography methods to trace the fiber links between them. A lower fractional anisotropy in this region makes it even difficult to track the fibers in the presence of noise. In this work, the authors focused on a stochastic approach to reconstruct these fiber pathways based on a Bayesian regularization framework. To estimate the true fiber direction (propagation vector), the a priori and conditional probability density functions are calculated in advance and are modeled as multivariate normal. The variance of the estimated tensor element vector is associated with the uncertainty due to noise and partial volume averaging (PVA). An adaptive and multiple sampling of the estimated tensor element vector, which is a function of the pre-estimated variance, overcomes the effect of noise and PVA in this work. The algorithm has been rigorously tested using a variety of synthetic data sets. The quantitative comparison of the results to standard algorithms motivated the authors to implement it for in vivo DTI data analysis. The algorithm has been implemented to delineate fibers in two major language pathways (Broca's to SMA and Broca's to Wernicke's) across 12 healthy subjects. Though the mean of standard deviation was marginally bigger than conventional (Euler's) approach [P. J. Basser et al., "In vivo fiber tractography using DT-MRI data," Magn. Reson. Med. 44(4), 625-632 (2000)], the number of extracted fibers in this approach was significantly higher. The authors also compared the performance of the proposed method to Lu's method [Y. Lu et al., "Improved fiber tractography with Bayesian
Rajaona, Harizo; Septier, François; Armand, Patrick; Delignon, Yves; Olry, Christophe; Albergel, Armand; Moussafir, Jacques
2015-12-01
In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.
Empirical Bayesian inference and model uncertainty
International Nuclear Information System (INIS)
Poern, K.
1994-01-01
This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability
Borths, Matthew R; Holroyd, Patricia A; Seiffert, Erik R
2016-01-01
recovered from each phylogenetic method, we reconstructed the biogeographic history of Hyaenodonta using parsimony optimization (PO), likelihood optimization (LO), and Bayesian Binary Markov chain Monte Carlo (MCMC) to examine support for the Afro-Arabian origin of Hyaenodonta. Across all analyses, we found that Hyaenodonta most likely originated in Europe, rather than Afro-Arabia. The clade is estimated by tip-dating analysis to have undergone a rapid radiation in the Late Cretaceous and Paleocene; a radiation currently not documented by fossil evidence. During the Paleocene, lineages are reconstructed as dispersing to Asia, Afro-Arabia, and North America. The place of origin of Hyainailouroidea is likely Afro-Arabia according to the Bayesian topologies but it is ambiguous using parsimony. All topologies support the constituent clades-Hyainailourinae, Apterodontinae, and Teratodontinae-as Afro-Arabian and tip-dating estimates that each clade is established in Afro-Arabia by the middle Eocene.
Three-dimensional total variation norm for SPECT reconstruction
International Nuclear Information System (INIS)
Persson, Mikael; Bone, Dianna; Elmqvist, H.
2001-01-01
The total variation (TV) norm has been described in literature as a method for reducing noise in two-dimensional (2D) images. At the same time, the TV-norm is very good at recovering edges in images, without introducing ringing or edge artefacts. It has also been proposed as a 2D regularisation function in Bayesian reconstruction, implemented in an expectation maximisation (EM) algorithm, and called TV-EM. The TV-EM was developed for 2D SPECT imaging, and the algorithm is capable of smoothing noise while maintaining edges without introducing artefacts. We have extended the TV-norm to take into account the third spatial dimension, and developed an iterative EM algorithm based on the three-dimensional (3D) TV-norm, which we call TV3D-EM. This takes into account the correlation between transaxial sections in SPECT, due to system resolution. We have compared the 2D and 3D algorithms using reconstructed images from simulated projection data. Phantoms used were a homogeneous sphere, and a 3D head phantom based on the Shepp-Logan phantom. The TV3D-EM algorithm yielded somewhat lower noise levels than TV-EM. The noise in the TV3D-EM had similar correlation in transaxial and longitudinal sections, which was not the case for TV-EM, or any 2D reconstruction method. In particular, longitudinal sections from TV3D-EM were perceived as less noisy when compared to TV-EM. The use of 3D reconstruction should also be advantageous if compensation for distant dependent collimator blurring is incorporated in the iterative algorithm
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
A brief history of plastic surgery in Iran.
Kalantar-Hormozi, Abdoljalil
2013-03-01
Although the exact time of performing plastic surgery is not addressed in the medical and historical literature, it can be supposed that these surgical procedures have a long and fascinating history. Recent excavations provided many documents regarding the application of medical instruments, surgical and even reconstructive procedures during the pre-historic and ancient periods. Actually, there is no historical definite time-zone separating general and cosmetic operations in the pre-modern time; however, historically there have been many surgeons who tried to perform reconstructive procedures during their usual medical practice. This article presents a brief look at the history of plastic surgery form the ancient to the contemporary era, with a special focus on Iran.
Objective Bayesianism and the Maximum Entropy Principle
Directory of Open Access Journals (Sweden)
Jon Williamson
2013-09-01
Full Text Available Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities; they should be calibrated to our evidence of physical probabilities; and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism are usually justified in different ways. In this paper, we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem.
Classifying emotion in Twitter using Bayesian network
Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya
2018-03-01
Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.
Brophy, Robert H; Gill, Corey S; Lyman, Stephen; Barnes, Ronnie P; Rodeo, Scott A; Warren, Russell F
2009-11-01
Meniscal and anterior cruciate ligament (ACL) injuries are common in college football athletes. The effect of meniscectomy and/or ACL surgery on the length of an athlete's career in the National Football League (NFL) has not been well examined. Athletes with a history of meniscectomy or ACL surgery before the NFL combine have a shorter career than matched controls. Case-control study; Level of evidence, 3. A database containing the injury history and career NFL statistics of athletes from 1987-2000 was used to match athletes with a history of meniscectomy and/or ACL surgery, and no other surgery or major injury, to controls without previous surgeries. Athletes were matched by position, year drafted, round drafted, and additional injury history. Fifty-four athletes with a history of meniscectomy, 29 with a history of ACL reconstruction, and 11 with a history of both were identified and matched with controls. Isolated meniscectomy reduced the length of career in years (5.6 vs 7.0; P = .03) and games played (62 vs 85; P = .02). Isolated ACL surgery did not significantly reduce the length of career in years or games played. Comparing the athletes with meniscectomy or ACL reconstruction to athletes with combined ACL reconstruction and meniscectomy, a history of both surgeries, resulted in a shorter career in games started (7.9 vs 35.1; P history of either surgery alone. A history of meniscectomy, but not ACL reconstruction, shortens the expected career of a professional football player. A combination of ACL reconstruction and meniscectomy may be more detrimental to an athlete's durability than either surgery alone. Further research is warranted to better understand how these injuries and surgeries affect an athlete's career and what can be done to improve the long-term outcome after treatment.
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
Andre; C. R. Martins
2006-11-01
Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.
Maximum a posteriori reconstruction of the Patlak parametric image from sinograms in dynamic PET
International Nuclear Information System (INIS)
Wang Guobao; Fu Lin; Qi Jinyi
2008-01-01
Parametric imaging using the Patlak graphical method has been widely used to analyze dynamic PET data. Conventionally a Patlak parametric image is generated by reconstructing a sequence of dynamic images first and then performing Patlak graphical analysis on the time-activity curves pixel-by-pixel. However, because it is rather difficult to model the noise distribution in reconstructed images, the spatially variant noise correlation is simply ignored in the Patlak analysis, which leads to sub-optimal results. In this paper we present a Bayesian method for reconstructing Patlak parametric images directly from raw sinogram data by incorporating the Patlak plot model into the image reconstruction procedure. A preconditioned conjugate gradient algorithm is used to find the maximum a posteriori solution. The proposed direct method is statistically more efficient than the conventional indirect approach because the Poisson noise distribution in PET data can be accurately modeled in the direct reconstruction. The computation cost of the direct method is similar to reconstruction time of two dynamic frames. Therefore, when more than two dynamic frames are used in the Patlak analysis, the direct method is faster than the conventional indirect approach. We conduct computer simulations to validate the proposed direct method. Comparisons with the conventional indirect approach show that the proposed method results in a more accurate estimate of the parametric image. The proposed method has been applied to dynamic fully 3D PET data from a microPET scanner
International Nuclear Information System (INIS)
Stawinski, G.
1998-01-01
Bayesian algorithms are developed to solve inverse problems in gamma imaging and photofission tomography. The first part of this work is devoted to the modeling of our measurement systems. Two models have been found for both applications: the first one is a simple conventional model and the second one is a cascaded point process model. EM and MCMC Bayesian algorithms for image restoration and image reconstruction have been developed for these models and compared. The cascaded point process model does not improve significantly the results previously obtained by the classical model. To original approaches have been proposed, which increase the results previously obtained. The first approach uses an inhomogeneous Markov Random Field as a prior law, and makes the regularization parameter spatially vary. However, the problem of the estimation of hyper-parameters has not been solved. In the case of the deconvolution of point sources, a second approach has been proposed, which introduces a high level prior model. The picture is modeled as a list of objects, whose parameters and number are unknown. The results obtained with this method are more accurate than those obtained with the conventional Markov Random Field prior model and require less computational costs. (author)
Bayesian data analysis in population ecology: motivations, methods, and benefits
Dorazio, Robert
2016-01-01
During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.
Fox, Gerardus J.A.; van den Berg, Stéphanie Martine; Veldkamp, Bernard P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item
Learning Bayesian Networks with Incomplete Data by Augmentation
Adel, Tameem; de Campos, Cassio P.
2016-01-01
We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...
Search for B+ --> mu+ nu_mu With Inclusive Reconstruction at BaBar
Energy Technology Data Exchange (ETDEWEB)
Aubert, Bernard; Bona, M.; Karyotakis, Y.; Lees, J.P.; Poireau, V.; Prencipe, E.; Prudent, X.; Tisserand, V.; /Annecy, LAPP; Garra Tico, J.; Grauges, E.; /Barcelona U., ECM; Lopez, L.; Palano, Antimo; Pappagallo, M.; /Bari U. /INFN, Bari; Eigen, G.; Stugu, Bjarne; Sun, L.; /Bergen U.; Abrams, G.S.; Battaglia, M.; Brown, D.N.; Cahn, Robert N.; Jacobsen, R.G.; /LBL, Berkeley /Birmingham U. /Ruhr U., Bochum /Bristol U. /British Columbia U. /Brunel U. /Novosibirsk, IYF /UC, Irvine /UCLA /UC, Riverside /UC, San Diego /UC, Santa Barbara /UC, Santa Cruz /Caltech /Cincinnati U. /Colorado U. /Colorado State U. /Dortmund U. /Dresden, Tech. U. /Ecole Polytechnique /Edinburgh U. /Ferrara U. /INFN, Ferrara /Frascati /Genoa U. /INFN, Genoa /Harvard U. /Heidelberg U. /Humboldt U., Berlin /Imperial Coll., London /Iowa U. /Iowa State U. /Johns Hopkins U. /Orsay, LAL /LLNL, Livermore /Liverpool U. /Queen Mary, U. of London /Royal Holloway, U. of London /Louisville U. /Mainz U., Inst. Kernphys. /Manchester U. /Maryland U. /Massachusetts U., Amherst /MIT /McGill U. /Consorzio Milano Ricerche /INFN, Milan /Mississippi U. /Montreal U. /Mt. Holyoke Coll. /Napoli Seconda U. /INFN, Naples /NIKHEF, Amsterdam /Notre Dame U. /Ohio State U. /Oregon U. /Padua U. /INFN, Padua /Paris U., VI-VII /Pennsylvania U. /Perugia U. /INFN, Perugia /INFN, Pisa /Princeton U. /Banca di Roma /Frascati /Rostock U. /Rutherford /DAPNIA, Saclay /South Carolina U. /SLAC /Stanford U., Phys. Dept. /SUNY, Albany /Tennessee U. /Texas U. /Texas U., Dallas /Turin U. /INFN, Turin /Trieste U. /INFN, Trieste /Valencia U., IFIC /Victoria U. /Warwick U. /Wisconsin U., Madison
2008-08-01
We search for the purely leptonic decay B{sup {+-}} {yields} {mu}{sup {+-}}{nu}{sub {mu}} in the full BABAR dataset, having an integrated luminosity of approximately 426 fb{sup -1}. We adopt a fully inclusive approach, where the signal candidate is identified by the highest momentum lepton in the event and the companion B is inclusively reconstructed without trying to identify its decay products. We set a preliminary upper limit on the branching fraction of {Beta}(B{sup {+-}} {yields} {mu}{sup {+-}}{nu}{sub {mu}}) < 1.3 x 10{sup -6} at the 90% confidence level, using a Bayesian approach.
Salenbien, W.; Baker, P. A.; Fritz, S. C.; Guedron, S.
2014-12-01
Lake Titicaca is one of the most important archives of paleoclimate in tropical South America, and prior studies have elucidated patterns of climate variation at varied temporal scales over the past 0.5 Ma. Yet, slow sediment accumulation rates in the main deeper basin of the lake have precluded analysis of the lake's most recent history at high resolution. To obtain a paleoclimate record of the last few millennia at multi-decadal resolution, we obtained five short cores, ranging from 139 to 181 cm in length, from the shallower Wiñaymarka sub-basin of of Lake Titicaca, where sedimentation rates are higher than in the lake's main basin. Selected cores have been analyzed for their geochemical signature by scanning XRF, diatom stratigraphy, sedimentology, and for 14C age dating. A total of 72 samples were 14C-dated using a Gas Ion Source automated high-throughput method for carbonate samples (mainly Littoridina sp. and Taphius montanus gastropod shells) at NOSAMS (Woods Hole Oceanographic Institute) with an analytical precision higher than 2%. The method has lower analytical precision compared with traditional AMS radiocarbon dating, but the lower cost enables analysis of a larger number of samples, and the error associated with the lower precision is relatively small for younger samples (< ~8,000 years). A 172-cm-long core was divided into centimeter long sections, and 47 14C dates were obtained from 1-cm intervals, averaging one date every 3-4 cm. The other cores were radiocarbon dated with a sparser sampling density that focused on visual unconformities and shell beds. The high-resolution radiocarbon analysis reveals complex sedimentation patterns in visually continuous sections, with abundant indicators of bioturbated or reworked sediments and periods of very rapid sediment accumulation. These features are not evident in the sparser sampling strategy but have significant implications for reconstructing past lake level and paleoclimatic history.
Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics
Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.
2018-03-01
Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre - stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.
Bayesian optimization for computationally extensive probability distributions.
Tamura, Ryo; Hukushima, Koji
2018-01-01
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.
An Intuitive Dashboard for Bayesian Network Inference
International Nuclear Information System (INIS)
Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V
2014-01-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++
An Intuitive Dashboard for Bayesian Network Inference
Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.
2014-03-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.
Energy Technology Data Exchange (ETDEWEB)
Schoelzel, C. [Bonn Univ. (Germany). Meteorologisches Inst.
2006-07-01
This thesis presents the development of statistical climatological-botanical transfer functions in order to provide reconstructions of Holocene climate variability in the Near East region. Two classical concepts, the biomisation as well as the indicator taxa approach, are translated into a Bayesian network. Fossil pollen spectra of laminated sediments from the Ein Gedi location at the western shoreline of the Dead Sea and from the crater lake Birkat Ram in the northern Golan serve as proxy data, covering the past 10000 and 6500 years, respectively. The climatological variables are winter temperature, summer temperature, and annual precipitation, obtained from the 0.5 x 0.5 degree climatology CRU TS 1.0. The Bayesian biome model is based on the three main vegetation territories, the Mediterranean, the Irano-Turanian, and the Saharo-Arabian territory, which are digitized on the same grid as the climate data. From their spatial extend, a classification in the phase space is described by estimating the conditional probability for the existence of a certain biome given the climate. These biome specific likelihood functions are modelled by a generalised linear model, including second order monomials of the climate variables. A statistical mixture model is applied to the biome probabilities as estimated by the Ein Gedi data, resulting in a posterior probability density function for the three dimensional climate state vector. The indicator taxa model is based on the distribution of 15 Mediterranean taxa. Their spatial extend allows to estimate the taxon specific likelihood functions. In this case, they are conditional probability density functions for the climate state vector given the existence of a certain taxon. In order to address the general problem of multivariate non-normally distributed populations, multivariate normal Copulas are used, which allow to create distribution functions with gamma as well as normal marginal distributions. Applying the model to the Birkat
International Nuclear Information System (INIS)
Ma, Mingming; Hu, Shouyun; Cao, Liwan; Appel, Erwin; Wang, Longsheng
2015-01-01
We studied magnetic and chemical parameters of sediments from sediments of a water reservoir at Linfen (China) in order to quantitatively reconstruct the atmospheric pollution history in this region. The results show that the main magnetic phases are magnetite and maghemite originating from the surrounding catchment and from anthropogenic activities, and there is a significant positive relationship between magnetic concentration parameters and heavy metals concentrations, indicating that magnetic proxies can be used to monitor the anthropogenic pollution. In order to uncover the atmospheric pollution history, we combined the known events of environmental improvement with variations of magnetic susceptibility (χ) and heavy metals along the cores to obtain a detailed chronological framework. In addition, air comprehensive pollution index (ACPI) was reconstructed from regression equation among magnetic and chemical parameters as well as atmospheric monitoring data. Based on these results, the atmospheric pollution history was successfully reconstructed. - Highlights: • Magnetic proxies can be used to monitor the heavy mental pollution in sediments. • Accurate age model was obtained using known events of environmental improvement. • Regression equation was obtained among sediment records and monitoring data. • Atmospheric pollution history was quantitatively reconstructed. - Atmospheric pollution history was quantitatively reconstructed using magnetic and chemical records of reservoir sediments combined with atmospheric monitoring data
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
Hepp, Johannes; Tuthorn, Mario; Zech, Roland; Mügler, Ines; Schlütz, Frank; Zech, Wolfgang; Zech, Michael
2015-10-01
Over the past decades, δ18O and δ2H analyses of lacustrine sediments became an invaluable tool in paleohydrology and paleolimnology for reconstructing the isotopic composition of past lake water and precipitation. However, based on δ18O or δ2H records alone, it can be challenging to distinguish between changes of the precipitation signal and changes caused by evaporation. Here we propose a coupled δ18O-δ2H biomarker approach that provides the possibility to disentangle between these two factors. The isotopic composition of long chain n-alkanes (n-C25, n-C27, n-C29, n-C31) were analyzed in order to establish a 16 ka Late Glacial and Holocene δ2H record for the sediment archive of Lake Panch Pokhari in High Himalaya, Nepal. The δ2Hn-alkane record generally corroborates a previously established δ18Osugar record reporting on high values characterizing the deglaciation and the Older and the Younger Dryas, and low values characterizing the Bølling and the Allerød periods. Since the investigated n-alkane and sugar biomarkers are considered to be primarily of aquatic origin, they were used to reconstruct the isotopic composition of lake water. The reconstructed deuterium excess of lake water ranges from +57‰ to -85‰ and is shown to serve as proxy for the evaporation history of Lake Panch Pokhari. Lake desiccation during the deglaciation, the Older Dryas and the Younger Dryas is affirmed by a multi-proxy approach using the Hydrogen Index (HI) and the carbon to nitrogen ratio (C/N) as additional proxies for lake sediment organic matter mineralization. Furthermore, the coupled δ18O and δ2H approach allows disentangling the lake water isotopic enrichment from variations of the isotopic composition of precipitation. The reconstructed 16 ka δ18Oprecipitation record of Lake Panch Pokhari is well in agreement with the δ18O records of Chinese speleothems and presumably reflects the Indian Summer Monsoon variability.
Vita-Finzi, Claudio
2012-05-13
During the last half century, advances in geomorphology-abetted by conceptual and technical developments in geophysics, geochemistry, remote sensing, geodesy, computing and ecology-have enhanced the potential value of fluvial history for reconstructing erosional and depositional sequences on the Earth and on Mars and for evaluating climatic and tectonic changes, the impact of fluvial processes on human settlement and health, and the problems faced in managing unstable fluvial systems. This journal is © 2012 The Royal Society
Bayesian Statistical Analysis of Historical and Late Holocene Rates of Sea-Level Change
Cahill, Niamh; Parnell, Andrew; Kemp, Andrew; Horton, Benjamin
2014-05-01
A fundamental concern associated with climate change is the rate at which sea levels are rising. Studies of past sea level (particularly beyond the instrumental data range) allow modern sea-level rise to be placed in a more complete context. Considering this, we perform a Bayesian statistical analysis on historical and late Holocene rates of sea-level change. The data that form the input to the statistical model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. The aims are to estimate rates of sea-level rise, to determine when modern rates of sea-level rise began and to observe how these rates have been changing over time. Many of the current methods for doing this use simple linear regression to estimate rates. This is often inappropriate as it is too rigid and it can ignore uncertainties that arise as part of the data collection exercise. This can lead to over confidence in the sea-level trends being characterized. The proposed Bayesian model places a Gaussian process prior on the rate process (i.e. the process that determines how rates of sea-level are changing over time). The likelihood of the observed data is the integral of this process. When dealing with proxy reconstructions, this is set in an errors-in-variables framework so as to take account of age uncertainty. It is also necessary, in this case, for the model to account for glacio-isostatic adjustment, which introduces a covariance between individual age and sea-level observations. This method provides a flexible fit and it allows for the direct estimation of the rate process with full consideration of all sources of uncertainty. Analysis of tide-gauge datasets and proxy reconstructions in this way means that changing rates of sea level can be estimated more comprehensively and accurately than previously possible. The model captures the continuous and dynamic evolution of sea-level change and results show that not only are modern sea levels rising but that the rates
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...
Directory of Open Access Journals (Sweden)
Ilaria A M Marino
Full Text Available A precise inference of past demographic histories including dating of demographic events using bayesian methods can only be achieved with the use of appropriate molecular rates and evolutionary models. Using a set of 596 mitochondrial cytochrome c oxidase I (COI sequences of two sister species of European green crabs of the genus Carcinus (C. maenas and C. aestuarii, our study shows how chronologies of past evolutionary events change significantly with the application of revised molecular rates that incorporate biogeographic events for calibration and appropriate demographic priors. A clear signal of demographic expansion was found for both species, dated between 10,000 and 20,000 years ago, which places the expansions events in a time frame following the Last Glacial Maximum (LGM. In the case of C. aestuarii, a population expansion was only inferred for the Adriatic-Ionian, suggestive of a colonization event following the flooding of the Adriatic Sea (18,000 years ago. For C. maenas, the demographic expansion inferred for the continental populations of West and North Europe might result from a northward recolonization from a southern refugium when the ice sheet retreated after the LGM. Collectively, our results highlight the importance of using adequate calibrations and demographic priors in order to avoid considerable overestimates of evolutionary time scales.
Goolsby, Eric W
2017-04-01
Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non-Brownian models, missing data, and within-species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time-consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000-species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within-species variation, non-Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time-consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation-Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars
Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review
Directory of Open Access Journals (Sweden)
Lsxmikant-Sambhaji Kokate
2011-07-01
Full Text Available Statistics uses two major approaches- conventional (or frequentist and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems.
Bayesian non- and semi-parametric methods and applications
Rossi, Peter
2014-01-01
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number
B-Spline potential function for maximum a-posteriori image reconstruction in fluorescence microscopy
Directory of Open Access Journals (Sweden)
Shilpa Dilipkumar
2015-03-01
Full Text Available An iterative image reconstruction technique employing B-Spline potential function in a Bayesian framework is proposed for fluorescence microscopy images. B-splines are piecewise polynomials with smooth transition, compact support and are the shortest polynomial splines. Incorporation of the B-spline potential function in the maximum-a-posteriori reconstruction technique resulted in improved contrast, enhanced resolution and substantial background reduction. The proposed technique is validated on simulated data as well as on the images acquired from fluorescence microscopes (widefield, confocal laser scanning fluorescence and super-resolution 4Pi microscopy. A comparative study of the proposed technique with the state-of-art maximum likelihood (ML and maximum-a-posteriori (MAP with quadratic potential function shows its superiority over the others. B-Spline MAP technique can find applications in several imaging modalities of fluorescence microscopy like selective plane illumination microscopy, localization microscopy and STED.
Non-linear Bayesian update of PCE coefficients
Litvinenko, Alexander
2014-01-06
Given: a physical system modeled by a PDE or ODE with uncertain coefficient q(?), a measurement operator Y (u(q), q), where u(q, ?) uncertain solution. Aim: to identify q(?). The mapping from parameters to observations is usually not invertible, hence this inverse identification problem is generally ill-posed. To identify q(!) we derived non-linear Bayesian update from the variational problem associated with conditional expectation. To reduce cost of the Bayesian update we offer a unctional approximation, e.g. polynomial chaos expansion (PCE). New: We apply Bayesian update to the PCE coefficients of the random coefficient q(?) (not to the probability density function of q).
Non-linear Bayesian update of PCE coefficients
Litvinenko, Alexander; Matthies, Hermann G.; Pojonk, Oliver; Rosic, Bojana V.; Zander, Elmar
2014-01-01
Given: a physical system modeled by a PDE or ODE with uncertain coefficient q(?), a measurement operator Y (u(q), q), where u(q, ?) uncertain solution. Aim: to identify q(?). The mapping from parameters to observations is usually not invertible, hence this inverse identification problem is generally ill-posed. To identify q(!) we derived non-linear Bayesian update from the variational problem associated with conditional expectation. To reduce cost of the Bayesian update we offer a unctional approximation, e.g. polynomial chaos expansion (PCE). New: We apply Bayesian update to the PCE coefficients of the random coefficient q(?) (not to the probability density function of q).
Bayesians versus frequentists a philosophical debate on statistical reasoning
Vallverdú, Jordi
2016-01-01
This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a concept introduced in the book’s final section. This monograph will be of interest to philosophers and historians of science and students in related fields. Despite the mathematical nature of the topic, no statistical background is required, making the book a valuable read for anyone interested in the history of statistics and human cognition.
Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith
2018-01-02
Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error...
A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research
Van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
A gentle introduction to Bayesian analysis : Applications to developmental research
van de Schoot, R.; Kaplan, D.; Denissen, J.J.A.; Asendorpf, J.B.; Neyer, F.J.; van Aken, M.A.G.
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
Reconstructing ATLAS SU3 in the CMSSM and relaxed phenomenological supersymmetry models
Fowlie, Andrew
2011-01-01
Assuming that the LHC makes a positive end-point measurement indicative of low-energy supersymmetry, we examine the prospects of reconstructing the parameter values of a typical low-mass point in the framework of the Constrained MSSM and in several other supersymmetry models that have more free parameters and fewer assumptions than the CMSSM. As a case study, we consider the ATLAS SU3 benchmark point with a Bayesian approach and with a Gaussian approximation to the likelihood for the measured masses and mass differences. First we investigate the impact of the hypothetical ATLAS measurement alone and show that it significantly narrows the confidence intervals of relevant, otherwise fairly unrestricted, model parameters. Next we add information about the relic density of neutralino dark matter to the likelihood and show that this further narrows the confidence intervals. We confirm that the CMSSM has the best prospects for parameter reconstruction; its results had little dependence on our choice of prior, in co...
A nonparametric Bayesian approach for genetic evaluation in ...
African Journals Online (AJOL)
South African Journal of Animal Science ... the Bayesian and Classical models, a Bayesian procedure is provided which allows these random ... data from the Elsenburg Dormer sheep stud and data from a simulation experiment are utilized. >
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Bayesian probability theory and inverse problems
International Nuclear Information System (INIS)
Kopec, S.
1994-01-01
Bayesian probability theory is applied to approximate solving of the inverse problems. In order to solve the moment problem with the noisy data, the entropic prior is used. The expressions for the solution and its error bounds are presented. When the noise level tends to zero, the Bayesian solution tends to the classic maximum entropy solution in the L 2 norm. The way of using spline prior is also shown. (author)
Variations on Bayesian Prediction and Inference
2016-05-09
inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle
Bayesian inference for psychology. Part II: Example applications with JASP.
Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D
2018-02-01
Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.
Improving Transparency and Replication in Bayesian Statistics : The WAMBS-Checklist
Depaoli, Sarah; van de Schoot, Rens
2017-01-01
Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at
An introduction to using Bayesian linear regression with clinical data.
Baldwin, Scott A; Larson, Michael J
2017-11-01
Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Using Bayesian belief networks in adaptive management.
J.B. Nyberg; B.G. Marcot; R. Sulyma
2006-01-01
Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...
Climate reconstruction from borehole temperatures influenced by groundwater flow
Kurylyk, B.; Irvine, D. J.; Tang, W.; Carey, S. K.; Ferguson, G. A. G.; Beltrami, H.; Bense, V.; McKenzie, J. M.; Taniguchi, M.
2017-12-01
Borehole climatology offers advantages over other climate reconstruction methods because further calibration steps are not required and heat is a ubiquitous subsurface property that can be measured from terrestrial boreholes. The basic theory underlying borehole climatology is that past surface air temperature signals are reflected in the ground surface temperature history and archived in subsurface temperature-depth profiles. High frequency surface temperature signals are attenuated in the shallow subsurface, whereas low frequency signals can be propagated to great depths. A limitation of analytical techniques to reconstruct climate signals from temperature profiles is that they generally require that heat flow be limited to conduction. Advection due to groundwater flow can thermally `contaminate' boreholes and result in temperature profiles being rejected for regional climate reconstructions. Although groundwater flow and climate change can result in contrasting or superimposed thermal disturbances, groundwater flow will not typically remove climate change signals in a subsurface thermal profile. Thus, climate reconstruction is still possible in the presence of groundwater flow if heat advection is accommodated in the conceptual and mathematical models. In this study, we derive a new analytical solution for reconstructing surface temperature history from borehole thermal profiles influenced by vertical groundwater flow. The boundary condition for the solution is composed of any number of sequential `ramps', i.e. periods with linear warming or cooling rates, during the instrumented and pre-observational periods. The boundary condition generation and analytical temperature modeling is conducted in a simple computer program. The method is applied to reconstruct climate in Winnipeg, Canada and Tokyo, Japan using temperature profiles recorded in hydrogeologically active environments. The results demonstrate that thermal disturbances due to groundwater flow and climate
Doing bayesian data analysis a tutorial with R and BUGS
Kruschke, John K
2011-01-01
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all
Goetschius, John; Hart, Joseph M
2016-01-01
When returning to physical activity, patients with a history of anterior cruciate ligament reconstruction (ACL-R) often experience limitations in knee-joint function that may be due to chronic impairments in quadriceps motor control. Assessment of knee-extension torque variability may demonstrate underlying impairments in quadriceps motor control in patients with a history of ACL-R. To identify differences in maximal isometric knee-extension torque variability between knees that have undergone ACL-R and healthy knees and to determine the relationship between knee-extension torque variability and self-reported knee function in patients with a history of ACL-R. Descriptive laboratory study. Laboratory. A total of 53 individuals with primary, unilateral ACL-R (age = 23.4 ± 4.9 years, height = 1.7 ± 0.1 m, mass = 74.6 ± 14.8 kg) and 50 individuals with no history of substantial lower extremity injury or surgery who served as controls (age = 23.3 ± 4.4 years, height = 1.7 ± 0.1 m, mass = 67.4 ± 13.2 kg). Torque variability, strength, and central activation ratio (CAR) were calculated from 3-second maximal knee-extension contraction trials (90° of flexion) with a superimposed electrical stimulus. All participants completed the International Knee Documentation Committee (IKDC) Subjective Knee Evaluation Form, and we determined the number of months after surgery. Group differences were assessed using independent-samples t tests. Correlation coefficients were calculated among torque variability, strength, CAR, months after surgery, and IKDC scores. Torque variability, strength, CAR, and months after surgery were regressed on IKDC scores using stepwise, multiple linear regression. Torque variability was greater and strength, CAR, and IKDC scores were lower in the ACL-R group than in the control group (P Torque variability and strength were correlated with IKDC scores (P Torque variability, strength, and CAR were correlated with each other (P Torque variability alone
Bayesian estimation of dose rate effectiveness
International Nuclear Information System (INIS)
Arnish, J.J.; Groer, P.G.
2000-01-01
A Bayesian statistical method was used to quantify the effectiveness of high dose rate 137 Cs gamma radiation at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice. The Bayesian approach considers both the temporal and dose dependence of radiation carcinogenesis and total mortality. This paper provides the first direct estimation of dose rate effectiveness using Bayesian statistics. This statistical approach provides a quantitative description of the uncertainty of the factor characterising the dose rate in terms of a probability density function. The results show that a fixed dose from 137 Cs gamma radiation delivered at a high dose rate is more effective at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice than the same dose delivered at a low dose rate. (author)
BATSE gamma-ray burst line search. 2: Bayesian consistency methodology
Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.
1994-01-01
We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.
Bayesian signal processing classical, modern, and particle filtering methods
Candy, James V
2016-01-01
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...
Bayesian Networks for Modeling Dredging Decisions
2011-10-01
years, that algorithms have been developed to solve these problems efficiently. Most modern Bayesian network software uses junction tree (a.k.a. join... software was used to develop the network . This is by no means an exhaustive list of Bayesian network applications, but it is representative of recent...characteristic node (SCN), state- defining node ( SDN ), effect node (EFN), or value node. The five types of nodes can be described as follows: ERDC/EL TR-11
A Bayesian classifier for symbol recognition
Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick
2007-01-01
URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...
Bayesian emulation for optimization in multi-step portfolio decisions
Irie, Kaoru; West, Mike
2016-01-01
We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...
Šafanda, Jan
2018-03-01
Reconstructions of past ground surface temperature changes from temperature logs conducted in several hundred meter deep boreholes have proved to be a valuable independent source of information on climate variations over the last millennium. The reconstruction techniques have been evolving for more than two decades to extract optimally the climate signal of the last millennium contained in the temperature logs of different length performed in sites with different histories of the Last Glacial Cycle. This paper analyzes the method of the Last Glacial Cycle thermal effect removal from such borehole temperature profiles used by Beltrami et al. (2017, https://doi.org/10.1002/2016GL071317) in reconstructing the last 500 year history. I show that the reported results of additional warming in this period reconstructed from the corrected borehole data for North America are an artifact generated by the correction.
Bayesian Analysis for Penalized Spline Regression Using WinBUGS
Directory of Open Access Journals (Sweden)
Ciprian M. Crainiceanu
2005-09-01
Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
DEFF Research Database (Denmark)
Brouwer, Thomas; Frellsen, Jes; Liò, Pietro
2017-01-01
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...
Graca, Bożena; Staniszewska, Marta; Zakrzewska, Danuta; Zalewska, Tamara
2016-06-01
This paper reports the reconstruction of the pollution history of 4-tert-octylphenol (OP) and 4-nonylphenol (NP) in the Baltic Sea. Alkylphenols are endocrine-disrupting compound and therefore toxic to aquatic organisms. Sediment cores were collected from regions with relatively stable sedimentation conditions. The cores were dated by the (210)Pb method. The OP and NP were determined using HPLC-FL. The highest inventory of these compounds was observed in the Gotland Deep (610 μg m(2) of NP and 47 μg m(2) of OP) and the lowest-on the slope of the Gdansk Deep (24 μg m(2) of NP and 16 μg m(2) of OP). Such spatial distribution was probably, among other factors, the result of the uplift of the sea floor. The pollution trends of OP and NP in sediments coincided with the following: (1) the beginnings of eutrophication (1960s/1970s of the twentieth century) and (2) strong increase in the areal extent and volume of hypoxia and anoxia in the Baltic (present century).
Visual histories of decision processes for collaborative decision making
Kozlova, Karine
2016-01-01
Remembering, understanding and reconstructing past activities is a necessary part of any learning, sense-making or decision making process. It is also essential for any collaborative activity. This dissertation investigates the design and evaluation of systems to support decision remembering, understanding and reconstruction by groups and individuals. By conducting three qualitative case studies of small professional groups, we identify the critical activities where history functionality is n...
High-resolution elastic recoil detection utilizing Bayesian probability theory
International Nuclear Information System (INIS)
Neumaier, P.; Dollinger, G.; Bergmaier, A.; Genchev, I.; Goergens, L.; Fischer, R.; Ronning, C.; Hofsaess, H.
2001-01-01
Elastic recoil detection (ERD) analysis is improved in view of depth resolution and the reliability of the measured spectra. Good statistics at even low ion fluences is obtained utilizing a large solid angle of 5 msr at the Munich Q3D magnetic spectrograph and using a 40 MeV 197 Au beam. In this way the elemental depth profiles are not essentially altered during analysis even if distributions with area densities below 1x10 14 atoms/cm 2 are measured. As the energy spread due to the angular acceptance is fully eliminated by ion-optical and numerical corrections, an accurate and reliable apparatus function is derived. It allows to deconvolute the measured spectra using the adaptive kernel method, a maximum entropy concept in the framework of Bayesian probability theory. In addition, the uncertainty of the reconstructed spectra is quantified. The concepts are demonstrated at 13 C depth profiles measured at ultra-thin films of tetrahedral amorphous carbon (ta-C). Depth scales of those profiles are given with an accuracy of 1.4x10 15 atoms/cm 2
Directory of Open Access Journals (Sweden)
Christidis Les
2008-07-01
Full Text Available Abstract Background Little is known about the role ecological shifts play in the evolution of Neotropical radiations that have colonized a variety of environments. We here examine habitat shifts in the evolutionary history of Elaenia flycatchers, a Neotropical bird lineage that lives in a range of forest and open habitats. We evaluate phylogenetic relationships within the genus based on mitochondrial and nuclear DNA sequence data, and then employ parsimony-based and Bayesian methods to reconstruct preferences for a number of habitat types and migratory behaviour throughout the evolutionary history of the genus. Using a molecular clock approach, we date the most important habitat shifts. Results Our analyses resolve phylogenetic relationships among Elaenia species and confirm several species associations predicted by morphology while furnishing support for other taxon placements that are in conflict with traditional classification, such as the elevation of various Elaenia taxa to species level. While savannah specialism is restricted to one basal clade within the genus, montane forest was invaded from open habitat only on a limited number of occasions. Riparian growth may have been favoured early on in the evolution of the main Elaenia clade and subsequently been deserted on several occasions. Austral long-distance migratory behaviour evolved on several occasions. Conclusion Ancestral reconstructions of habitat preferences reveal pronounced differences not only in the timing of the emergence of certain habitat preferences, but also in the frequency of habitat shifts. The early origin of savannah specialism in Elaenia highlights the importance of this habitat in Neotropical Pliocene and late Miocene biogeography. While forest in old mountain ranges such as the Tepuis and the Brazilian Shield was colonized early on, the most important colonization event of montane forest was in conjunction with Pliocene Andean uplift. Riparian habitats may have
Microsurgical reconstruction of extensive oncological scalp defects
Directory of Open Access Journals (Sweden)
Ole eGoertz
2015-09-01
Full Text Available While most small to medium defects of the scalp can be covered by local flaps, large defects or complicating factors like a history of radiotherapy often require a microsurgical reconstruction.Several factors need to be considered in such procedures. A sufficient preoperative planning is based on adequate imaging of the malignancy and a multi-disciplinary concept. Several flaps are available for such reconstructions, of which the latissimus dorsi and anterior lateral thigh flaps are the most commonly used ones.In very large defects, combined flaps such as a parascapular / latissimus dorsi flaps can be highly useful or necessary. The most commonly used recipient vessels for microsurgical scalp reconstructions are the superficial temporal vessels, but various other feasible choices exist. If the concomitant veins are not sufficient, the jugular veins represent a safe backup alternative but require a vessel interposition or long pedicle. Postoperative care and patient positioning can be difficult in these patients but can be facilitated by various devices. Overall, microsurgical reconstruction of large scalp defects is a feasible undertaking if the mentioned key factors are taken into account.
Kaplan, David; Lee, Chansoon
2018-01-01
This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.
Stratigraphy and geologic history of Mercury
International Nuclear Information System (INIS)
Spudis, P.D.; Guest, J.E.
1988-01-01
The geologic evolution of Mercury based on the Mariner-10 mission data is discussed. As reconstructed through photogeological analysis of global geologic relations of rock-stratigraphic units, Mercury's geologic history is shown to involve intensive early impact bombardment and widespread resurfacing by volcanic lavas. Evidence is presented to indicate that this volcanic activity essentially ended as much as 3 Gyr ago, with most of the major geologic events being completed within the first 1 to 1.5 Gyr of Mercurian history
Stratigraphy and geologic history of Mercury
Spudis, Paul D.; Guest, John E.
1988-01-01
The geologic evolution of Mercury based on the Mariner-10 mission data is discussed. As reconstructed through photogeological analysis of global geologic relations of rock-stratigraphic units, Mercury's geologic history is shown to involve intensive early impact bombardment and widespread resurfacing by volcanic lavas. Evidence is presented to indicate that this volcanic activity essentially ended as much as 3 Gyr ago, with most of the major geologic events being completed within the first 1 to 1.5 Gyr of Mercurian history.
Can natural selection encode Bayesian priors?
Ramírez, Juan Camilo; Marshall, James A R
2017-08-07
The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly. Each individual should evolve to behave as if updating a prior estimate of the unknown environment variable to a posterior estimate as it collects evidence. The prior estimate represents the decision-maker's default belief regarding the environment variable, i.e., the individual's default 'worldview' of the environment. This default belief has been hypothesised to be shaped by natural selection and represent the environment experienced by the individual's ancestors. We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these 'Bayesian' individuals, we also
Mechanistic curiosity will not kill the Bayesian cat
Borsboom, D.; Wagenmakers, E.-J.; Romeijn, J.-W.
2011-01-01
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer
Mechanistic curiosity will not kill the Bayesian cat
Borsboom, Denny; Wagenmakers, Eric-Jan; Romeijn, Jan-Willem
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer
Non-homogeneous dynamic Bayesian networks for continuous data
Grzegorczyk, Marco; Husmeier, Dirk
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with
Statistics: a Bayesian perspective
National Research Council Canada - National Science Library
Berry, Donald A
1996-01-01
...: it is the only introductory textbook based on Bayesian ideas, it combines concepts and methods, it presents statistics as a means of integrating data into the significant process, it develops ideas...
Embedding the results of focussed Bayesian fusion into a global context
Sander, Jennifer; Heizmann, Michael
2014-05-01
Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.
A Bayesian Optimal Design for Sequential Accelerated Degradation Testing
Directory of Open Access Journals (Sweden)
Xiaoyang Li
2017-07-01
Full Text Available When optimizing an accelerated degradation testing (ADT plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures.
A Bayesian Method for Weighted Sampling
Lo, Albert Y.
1993-01-01
Bayesian statistical inference for sampling from weighted distribution models is studied. Small-sample Bayesian bootstrap clone (BBC) approximations to the posterior distribution are discussed. A second-order property for the BBC in unweighted i.i.d. sampling is given. A consequence is that BBC approximations to a posterior distribution of the mean and to the sampling distribution of the sample average, can be made asymptotically accurate by a proper choice of the random variables that genera...
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...
Bayesian inference for psychology. Part I : Theoretical advantages and practical ramifications
Wagenmakers, E.-J.; Marsman, M.; Jamil, T.; Ly, A.; Verhagen, J.; Love, J.; Selker, R.; Gronau, Q.F.; Šmíra, M.; Epskamp, S.; Matzke, D.; Rouder, J.N.; Morey, R.D.
2018-01-01
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete
Nguyen, Hung T. T.; Galelli, Stefano
2018-03-01
Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.
A tutorial introduction to Bayesian models of cognitive development.
Perfors, Amy; Tenenbaum, Joshua B; Griffiths, Thomas L; Xu, Fei
2011-09-01
We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science. Copyright © 2010 Elsevier B.V. All rights reserved.
iSEDfit: Bayesian spectral energy distribution modeling of galaxies
Moustakas, John
2017-08-01
iSEDfit uses Bayesian inference to extract the physical properties of galaxies from their observed broadband photometric spectral energy distribution (SED). In its default mode, the inputs to iSEDfit are the measured photometry (fluxes and corresponding inverse variances) and a measurement of the galaxy redshift. Alternatively, iSEDfit can be used to estimate photometric redshifts from the input photometry alone. After the priors have been specified, iSEDfit calculates the marginalized posterior probability distributions for the physical parameters of interest, including the stellar mass, star-formation rate, dust content, star formation history, and stellar metallicity. iSEDfit also optionally computes K-corrections and produces multiple "quality assurance" (QA) plots at each stage of the modeling procedure to aid in the interpretation of the prior parameter choices and subsequent fitting results. The software is distributed as part of the impro IDL suite.
Algorithms For Phylogeny Reconstruction In a New Mathematical Model
Lenzini, Gabriele; Marianelli, Silvia
1997-01-01
The evolutionary history of a set of species is represented by a tree called phylogenetic tree or phylogeny. Its structure depends on precise biological assumptions about the evolution of species. Problems related to phylogeny reconstruction (i.e., finding a tree representation of information
Study on shielded pump system failure analysis method based on Bayesian network
International Nuclear Information System (INIS)
Bao Yilan; Huang Gaofeng; Tong Lili; Cao Xuewu
2012-01-01
This paper applies Bayesian network to the system failure analysis, with an aim to improve knowledge representation of the uncertainty logic and multi-fault states in system failure analysis. A Bayesian network for shielded pump failure analysis is presented, conducting fault parameter learning, updating Bayesian network parameter based on new samples. Finally, through the Bayesian network inference, vulnerability in this system, the largest possible failure modes, and the fault probability are obtained. The powerful ability of Bayesian network to analyze system fault is illustrated by examples. (authors)
Korattikara, A.; Rathod, V.; Murphy, K.; Welling, M.; Cortes, C.; Lawrence, N.D.; Lee, D.D.; Sugiyama, M.; Garnett, R.
2015-01-01
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple
DEFF Research Database (Denmark)
Hartelius, Karsten; Carstensen, Jens Michael
2003-01-01
A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which r...
An introduction to Bayesian statistics in health psychology
Depaoli, Sarah; Rus, Holly; Clifton, James; van de Schoot, A.G.J.; Tiemensma, Jitske
2017-01-01
The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of Health Psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation
Ptolemy's Britain and Ireland: A New Digital Reconstruction
Abshire, Corey; Durham, Anthony; Gusev, Dmitri A.; Stafeyev, Sergey K.
2018-05-01
In this paper, we expand application of our mathematical methods for translating ancient coordinates from the classical Geography by Claudius Ptolemy into modern coordinates from India and Arabia to Britain and Ireland, historically important islands on the periphery of the ancient Roman Empire. The methods include triangulation and flocking with subsequent Bayesian correction. The results of our work can be conveniently visualized in modern GIS tools, such as ArcGIS, QGIS, and Google Earth. The enhancements we have made include a novel technique for handling tentatively identified points. We compare the precision of reconstruction achieved for Ptolemy's Britain and Ireland with the precisions that we had computed earlier for his India before the Ganges and three provinces of Arabia. We also provide improved validation and comparison amongst the methods applied. We compare our results with the prior work, while utilizing knowledge from such important ancient sources as the Antonine Itinerary, Tabula Peutingeriana, and the Ravenna Cosmography. The new digital reconstruction of Claudius Ptolemy's Britain and Ireland presented in this paper, along with the accompanying linguistic analysis of ancient toponyms, contributes to improvement of understanding of our cultural cartographic heritage by making it easier to study the ancient world using the popular and accessible GIS programs.
Bayesian estimation of the discrete coefficient of determination.
Chen, Ting; Braga-Neto, Ulisses M
2016-12-01
The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.
A Bayesian approach to particle identification in ALICE
CERN. Geneva
2016-01-01
Among the LHC experiments, ALICE has unique particle identification (PID) capabilities exploiting different types of detectors. During Run 1, a Bayesian approach to PID was developed and intensively tested. It facilitates the combination of information from different sub-systems. The adopted methodology and formalism as well as the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE will be reviewed. Results are presented with PID performed via measurements of specific energy loss (dE/dx) and time-of-flight using information from the TPC and TOF detectors, respectively. Methods to extract priors from data and to compare PID efficiencies and misidentification probabilities in data and Monte Carlo using high-purity samples of identified particles will be presented. Bayesian PID results were found consistent with previous measurements published by ALICE. The Bayesian PID approach gives a higher signal-to-background ratio and a similar or larger statist...
Optimal Detection under the Restricted Bayesian Criterion
Directory of Open Access Journals (Sweden)
Shujun Liu
2017-07-01
Full Text Available This paper aims to find a suitable decision rule for a binary composite hypothesis-testing problem with a partial or coarse prior distribution. To alleviate the negative impact of the information uncertainty, a constraint is considered that the maximum conditional risk cannot be greater than a predefined value. Therefore, the objective of this paper becomes to find the optimal decision rule to minimize the Bayes risk under the constraint. By applying the Lagrange duality, the constrained optimization problem is transformed to an unconstrained optimization problem. In doing so, the restricted Bayesian decision rule is obtained as a classical Bayesian decision rule corresponding to a modified prior distribution. Based on this transformation, the optimal restricted Bayesian decision rule is analyzed and the corresponding algorithm is developed. Furthermore, the relation between the Bayes risk and the predefined value of the constraint is also discussed. The Bayes risk obtained via the restricted Bayesian decision rule is a strictly decreasing and convex function of the constraint on the maximum conditional risk. Finally, the numerical results including a detection example are presented and agree with the theoretical results.
Bayesian approach and application to operation safety
International Nuclear Information System (INIS)
Procaccia, H.; Suhner, M.Ch.
2003-01-01
The management of industrial risks requires the development of statistical and probabilistic analyses which use all the available convenient information in order to compensate the insufficient experience feedback in a domain where accidents and incidents remain too scarce to perform a classical statistical frequency analysis. The Bayesian decision approach is well adapted to this problem because it integrates both the expertise and the experience feedback. The domain of knowledge is widen, the forecasting study becomes possible and the decisions-remedial actions are strengthen thanks to risk-cost-benefit optimization analyzes. This book presents the bases of the Bayesian approach and its concrete applications in various industrial domains. After a mathematical presentation of the industrial operation safety concepts and of the Bayesian approach principles, this book treats of some of the problems that can be solved thanks to this approach: softwares reliability, controls linked with the equipments warranty, dynamical updating of databases, expertise modeling and weighting, Bayesian optimization in the domains of maintenance, quality control, tests and design of new equipments. A synthesis of the mathematical formulae used in this approach is given in conclusion. (J.S.)
Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics
Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.
2018-06-01
Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2-D seismic reflection data processing flow focused on pre-stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching, to estimate the uncertainties of the depths of key horizons near the Deep Sea Drilling Project (DSDP) borehole 258 (DSDP-258) located in the Mentelle Basin, southwest of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ±2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent to the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program, leg 369.
Towards Bayesian Inference of the Fast-Ion Distribution Function
DEFF Research Database (Denmark)
Stagner, L.; Heidbrink, W.W.; Salewski, Mirko
2012-01-01
sensitivity of the measurements are incorporated into Bayesian likelihood probabilities, while prior probabilities enforce physical constraints. As an initial step, this poster uses Bayesian statistics to infer the DIII-D electron density profile from multiple diagnostic measurements. Likelihood functions....... However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and ``weight functions" that describe the phase space...
Bayesian Correlation Analysis for Sequence Count Data.
Directory of Open Access Journals (Sweden)
Daniel Sánchez-Taltavull
Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.
A Bayesian approach to meta-analysis of plant pathology studies.
Mila, A L; Ngugi, H K
2011-01-01
Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework
Wang, Zhaoshan; Du, Shuhui; Dayanandan, Selvadurai; Wang, Dongsheng; Zeng, Yanfei; Zhang, Jianguo
2014-01-01
Populus (Salicaceae) is one of the most economically and ecologically important genera of forest trees. The complex reticulate evolution and lack of highly variable orthologous single-copy DNA markers have posed difficulties in resolving the phylogeny of this genus. Based on a large data set of nuclear and plastid DNA sequences, we reconstructed robust phylogeny of Populus using parsimony, maximum likelihood and Bayesian inference methods. The resulting phylogenetic trees showed better resolution at both inter- and intra-sectional level than previous studies. The results revealed that (1) the plastid-based phylogenetic tree resulted in two main clades, suggesting an early divergence of the maternal progenitors of Populus; (2) three advanced sections (Populus, Aigeiros and Tacamahaca) are of hybrid origin; (3) species of the section Tacamahaca could be divided into two major groups based on plastid and nuclear DNA data, suggesting a polyphyletic nature of the section; and (4) many species proved to be of hybrid origin based on the incongruence between plastid and nuclear DNA trees. Reticulate evolution may have played a significant role in the evolution history of Populus by facilitating rapid adaptive radiations into different environments.
Directory of Open Access Journals (Sweden)
Zhaoshan Wang
Full Text Available Populus (Salicaceae is one of the most economically and ecologically important genera of forest trees. The complex reticulate evolution and lack of highly variable orthologous single-copy DNA markers have posed difficulties in resolving the phylogeny of this genus. Based on a large data set of nuclear and plastid DNA sequences, we reconstructed robust phylogeny of Populus using parsimony, maximum likelihood and Bayesian inference methods. The resulting phylogenetic trees showed better resolution at both inter- and intra-sectional level than previous studies. The results revealed that (1 the plastid-based phylogenetic tree resulted in two main clades, suggesting an early divergence of the maternal progenitors of Populus; (2 three advanced sections (Populus, Aigeiros and Tacamahaca are of hybrid origin; (3 species of the section Tacamahaca could be divided into two major groups based on plastid and nuclear DNA data, suggesting a polyphyletic nature of the section; and (4 many species proved to be of hybrid origin based on the incongruence between plastid and nuclear DNA trees. Reticulate evolution may have played a significant role in the evolution history of Populus by facilitating rapid adaptive radiations into different environments.
Inference in hybrid Bayesian networks
International Nuclear Information System (INIS)
Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio
2009-01-01
Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.
The image recognition based on neural network and Bayesian decision
Wang, Chugege
2018-04-01
The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.
Editorial: Bayesian benefits for child psychology and psychiatry researchers.
Oldehinkel, Albertine J
2016-09-01
For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.
The Wage Effects of Personal Smoking History
GRAFOVA, IRINA B.; STAFFORD, FRANK P.
2009-01-01
Why do we observe a wage differential between smokers and non-smokers? Pooling reports of current and prior smoking activity across 15 years from the Panel Study of Income Dynamics (PSID) allows the reconstruction of individual smoking histories. Dividing the sample into smoking history groups, the four largest of which are: persistent smokers, never smokers, former smokers, and future quitters reveals that there is no observed wage gap between former smokers and those who have never smoked. ...
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...
Bayesian-based localization in inhomogeneous transmission media
DEFF Research Database (Denmark)
Nadimi, E. S.; Blanes-Vidal, V.; Johansen, P. M.
2013-01-01
In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heteroge......In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network...... with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. © 2013 ACM....
Bayesian modeling of ChIP-chip data using latent variables.
Wu, Mingqi
2009-10-26
BACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the
Computing security strategies in finite horizon repeated Bayesian games
Lichun Li
2017-07-10
This paper studies security strategies in two-player zero-sum repeated Bayesian games with finite horizon. In such games, each player has a private type which is independently chosen according to a publicly known a priori probability. Players\\' types are fixed all through the game. The game is played for finite stages. At every stage, players simultaneously choose their actions which are observed by the public. The one-stage payoff of player 1 (or penalty to player 2) depends on both players types and actions, and is not directly observed by any player. While player 1 aims to maximize the total payoff over the game, player 2 wants to minimize it. This paper provides each player two ways to compute the security strategy, i.e. the optimal strategy in the worst case. First, a security strategy that directly depends on both players\\' history actions is derived by refining the sequence form. Noticing that history action space grows exponentially with respect to the time horizon, this paper further presents a security strategy that depends on player\\'s fixed sized sufficient statistics. The sufficient statistics is shown to consist of the belief on one\\'s own type, the regret on the other player\\'s type, and the stage, and is independent of the other player\\'s strategy.
Bayesian Hierarchical Random Effects Models in Forensic Science
Directory of Open Access Journals (Sweden)
Colin G. G. Aitken
2018-04-01
Full Text Available Statistical modeling of the evaluation of evidence with the use of the likelihood ratio has a long history. It dates from the Dreyfus case at the end of the nineteenth century through the work at Bletchley Park in the Second World War to the present day. The development received a significant boost in 1977 with a seminal work by Dennis Lindley which introduced a Bayesian hierarchical random effects model for the evaluation of evidence with an example of refractive index measurements on fragments of glass. Many models have been developed since then. The methods have now been sufficiently well-developed and have become so widespread that it is timely to try and provide a software package to assist in their implementation. With that in mind, a project (SAILR: Software for the Analysis and Implementation of Likelihood Ratios was funded by the European Network of Forensic Science Institutes through their Monopoly programme to develop a software package for use by forensic scientists world-wide that would assist in the statistical analysis and implementation of the approach based on likelihood ratios. It is the purpose of this document to provide a short review of a small part of this history. The review also provides a background, or landscape, for the development of some of the models within the SAILR package and references to SAILR as made as appropriate.
Bayesian Hierarchical Random Effects Models in Forensic Science.
Aitken, Colin G G
2018-01-01
Statistical modeling of the evaluation of evidence with the use of the likelihood ratio has a long history. It dates from the Dreyfus case at the end of the nineteenth century through the work at Bletchley Park in the Second World War to the present day. The development received a significant boost in 1977 with a seminal work by Dennis Lindley which introduced a Bayesian hierarchical random effects model for the evaluation of evidence with an example of refractive index measurements on fragments of glass. Many models have been developed since then. The methods have now been sufficiently well-developed and have become so widespread that it is timely to try and provide a software package to assist in their implementation. With that in mind, a project (SAILR: Software for the Analysis and Implementation of Likelihood Ratios) was funded by the European Network of Forensic Science Institutes through their Monopoly programme to develop a software package for use by forensic scientists world-wide that would assist in the statistical analysis and implementation of the approach based on likelihood ratios. It is the purpose of this document to provide a short review of a small part of this history. The review also provides a background, or landscape, for the development of some of the models within the SAILR package and references to SAILR as made as appropriate.
Fully probabilistic design of hierarchical Bayesian models
Czech Academy of Sciences Publication Activity Database
Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine
2016-01-01
Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross-entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf
Flood quantile estimation at ungauged sites by Bayesian networks
Mediero, L.; Santillán, D.; Garrote, L.
2012-04-01
Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a
Stone, Graham N; White, Sarah C; Csóka, György; Melika, George; Mutun, Serap; Pénzes, Zsolt; Sadeghi, S Ebrahim; Schönrogge, Karsten; Tavakoli, Majid; Nicholls, James A
2017-12-01
Approximate Bayesian computation (ABC) is a powerful and widely used approach in inference of population history. However, the computational effort required to discriminate among alternative historical scenarios often limits the set that is compared to those considered more likely a priori. While often justifiable, this approach will fail to consider unexpected but well-supported population histories. We used a hierarchical tournament approach, in which subsets of scenarios are compared in a first round of ABC analyses and the winners are compared in a second analysis, to reconstruct the population history of an oak gall wasp, Synergus umbraculus (Hymenoptera, Cynipidae) across the Western Palaearctic. We used 4,233 bp of sequence data across seven loci to explore the relationships between four putative Pleistocene refuge populations in Iberia, Italy, the Balkans and Western Asia. We compared support for 148 alternative scenarios in eight pools, each pool comprising all possible rearrangements of four populations over a given topology of relationships, with or without founding of one population by admixture and with or without an unsampled "ghost" population. We found very little support for the directional "out of the east" scenario previously inferred for other gall wasp community members. Instead, the best-supported models identified Iberia as the first-regional population to diverge from the others in the late Pleistocene, followed by divergence between the Balkans and Western Asia, and founding of the Italian population through late Pleistocene admixture from Iberia and the Balkans. We compare these results with what is known for other members of the oak gall community, and consider the strengths and weaknesses of using a tournament approach to explore phylogeographic model space. © 2017 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.
Bayesian estimation inherent in a Mexican-hat-type neural network
Takiyama, Ken
2016-05-01
Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.
Hierarchical Bayesian inference of the initial mass function in composite stellar populations
Dries, M.; Trager, S. C.; Koopmans, L. V. E.; Popping, G.; Somerville, R. S.
2018-03-01
The initial mass function (IMF) is a key ingredient in many studies of galaxy formation and evolution. Although the IMF is often assumed to be universal, there is continuing evidence that it is not universal. Spectroscopic studies that derive the IMF of the unresolved stellar populations of a galaxy often assume that this spectrum can be described by a single stellar population (SSP). To alleviate these limitations, in this paper we have developed a unique hierarchical Bayesian framework for modelling composite stellar populations (CSPs). Within this framework, we use a parametrized IMF prior to regulate a direct inference of the IMF. We use this new framework to determine the number of SSPs that is required to fit a set of realistic CSP mock spectra. The CSP mock spectra that we use are based on semi-analytic models and have an IMF that varies as a function of stellar velocity dispersion of the galaxy. Our results suggest that using a single SSP biases the determination of the IMF slope to a higher value than the true slope, although the trend with stellar velocity dispersion is overall recovered. If we include more SSPs in the fit, the Bayesian evidence increases significantly and the inferred IMF slopes of our mock spectra converge, within the errors, to their true values. Most of the bias is already removed by using two SSPs instead of one. We show that we can reconstruct the variable IMF of our mock spectra for signal-to-noise ratios exceeding ˜75.
A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.
Directory of Open Access Journals (Sweden)
Guillaume Bal
Full Text Available Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i an emotive simulated example, ii application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Bayesian estimation of regularization parameters for deformable surface models
International Nuclear Information System (INIS)
Cunningham, G.S.; Lehovich, A.; Hanson, K.M.
1999-01-01
In this article the authors build on their past attempts to reconstruct a 3D, time-varying bolus of radiotracer from first-pass data obtained by the dynamic SPECT imager, FASTSPECT, built by the University of Arizona. The object imaged is a CardioWest total artificial heart. The bolus is entirely contained in one ventricle and its associated inlet and outlet tubes. The model for the radiotracer distribution at a given time is a closed surface parameterized by 482 vertices that are connected to make 960 triangles, with nonuniform intensity variations of radiotracer allowed inside the surface on a voxel-to-voxel basis. The total curvature of the surface is minimized through the use of a weighted prior in the Bayesian framework, as is the weighted norm of the gradient of the voxellated grid. MAP estimates for the vertices, interior intensity voxels and background count level are produced. The strength of the priors, or hyperparameters, are determined by maximizing the probability of the data given the hyperparameters, called the evidence. The evidence is calculated by first assuming that the posterior is approximately normal in the values of the vertices and voxels, and then by evaluating the integral of the multi-dimensional normal distribution. This integral (which requires evaluating the determinant of a covariance matrix) is computed by applying a recent algorithm from Bai et. al. that calculates the needed determinant efficiently. They demonstrate that the radiotracer is highly inhomogeneous in early time frames, as suspected in earlier reconstruction attempts that assumed a uniform intensity of radiotracer within the closed surface, and that the optimal choice of hyperparameters is substantially different for different time frames
Comprehension and computation in Bayesian problem solving
Directory of Open Access Journals (Sweden)
Eric D. Johnson
2015-07-01
Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.
Being Bayesian in a quantum world
International Nuclear Information System (INIS)
Fuchs, C.
2005-01-01
Full text: To be a Bayesian about probability theory is to accept that probabilities represent subjective degrees of belief and nothing more. This is in distinction to the idea that probabilities represent long-term frequencies or objective propensities. But, how can a subjective account of probabilities coexist with the existence of quantum mechanics? To accept quantum mechanics is to accept the calculational apparatus of quantum states and the Born rule for determining probabilities in a quantum measurement. If there ever were a place for probabilities to be objective, it ought to be here. This raises the question of whether Bayesianism and quantum mechanics are compatible at all. For the Bayesian, it only suggests that we should rethink what quantum mechanics is about. Is it 'law of nature' or really more 'law of thought'? From transistors to lasers, the evidence is in that we live in a quantum world. One could infer from this that all the elements in the quantum formalism necessarily mirror nature itself: wave functions are so successful as calculational tools precisely because they represent elements of reality. A more Bayesian-like perspective is that if wave functions generate probabilities, then they too must be Bayesian degrees of belief, with all that such a radical idea entails. In particular, quantum probabilities have no firmer hold on reality than the word 'belief' in 'degrees of belief' already indicates. From this perspective, the only sense in which the quantum formalism mirrors nature is through the constraints it places on gambling agents who would like to better navigate through world. One might think that this is thin information, but it is not insubstantial. To the extent that an agent should use quantum mechanics for his uncertainty accounting rather than some other theory tells us something about the world itself - i.e., the world independent of the agent and his particular beliefs at any moment. In this talk, I will try to shore up these
Bayesian Noise Estimation for Non-ideal Cosmic Microwave Background Experiments
Wehus, I. K.; Næss, S. K.; Eriksen, H. K.
2012-03-01
We describe a Bayesian framework for estimating the time-domain noise covariance of cosmic microwave background (CMB) observations, typically parameterized in terms of a 1/f frequency profile. This framework is based on the Gibbs sampling algorithm, which allows for exact marginalization over nuisance parameters through conditional probability distributions. In this paper, we implement support for gaps in the data streams and marginalization over fixed time-domain templates, and also outline how to marginalize over confusion from CMB fluctuations, which may be important for high signal-to-noise experiments. As a by-product of the method, we obtain proper constrained realizations, which themselves can be useful for map making. To validate the algorithm, we demonstrate that the reconstructed noise parameters and corresponding uncertainties are unbiased using simulated data. The CPU time required to process a single data stream of 100,000 samples with 1000 samples removed by gaps is 3 s if only the maximum posterior parameters are required, and 21 s if one also wants to obtain the corresponding uncertainties by Gibbs sampling.
BAYESIAN NOISE ESTIMATION FOR NON-IDEAL COSMIC MICROWAVE BACKGROUND EXPERIMENTS
International Nuclear Information System (INIS)
Wehus, I. K.; Næss, S. K.; Eriksen, H. K.
2012-01-01
We describe a Bayesian framework for estimating the time-domain noise covariance of cosmic microwave background (CMB) observations, typically parameterized in terms of a 1/f frequency profile. This framework is based on the Gibbs sampling algorithm, which allows for exact marginalization over nuisance parameters through conditional probability distributions. In this paper, we implement support for gaps in the data streams and marginalization over fixed time-domain templates, and also outline how to marginalize over confusion from CMB fluctuations, which may be important for high signal-to-noise experiments. As a by-product of the method, we obtain proper constrained realizations, which themselves can be useful for map making. To validate the algorithm, we demonstrate that the reconstructed noise parameters and corresponding uncertainties are unbiased using simulated data. The CPU time required to process a single data stream of 100,000 samples with 1000 samples removed by gaps is 3 s if only the maximum posterior parameters are required, and 21 s if one also wants to obtain the corresponding uncertainties by Gibbs sampling.
BAYESIAN NOISE ESTIMATION FOR NON-IDEAL COSMIC MICROWAVE BACKGROUND EXPERIMENTS
Energy Technology Data Exchange (ETDEWEB)
Wehus, I. K. [Theoretical Physics, Imperial College London, London SW7 2AZ (United Kingdom); Naess, S. K.; Eriksen, H. K., E-mail: i.k.wehus@fys.uio.no, E-mail: sigurdkn@astro.uio.no, E-mail: h.k.k.eriksen@astro.uio.no [Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029, Blindern, N-0315 Oslo (Norway)
2012-03-01
We describe a Bayesian framework for estimating the time-domain noise covariance of cosmic microwave background (CMB) observations, typically parameterized in terms of a 1/f frequency profile. This framework is based on the Gibbs sampling algorithm, which allows for exact marginalization over nuisance parameters through conditional probability distributions. In this paper, we implement support for gaps in the data streams and marginalization over fixed time-domain templates, and also outline how to marginalize over confusion from CMB fluctuations, which may be important for high signal-to-noise experiments. As a by-product of the method, we obtain proper constrained realizations, which themselves can be useful for map making. To validate the algorithm, we demonstrate that the reconstructed noise parameters and corresponding uncertainties are unbiased using simulated data. The CPU time required to process a single data stream of 100,000 samples with 1000 samples removed by gaps is 3 s if only the maximum posterior parameters are required, and 21 s if one also wants to obtain the corresponding uncertainties by Gibbs sampling.
Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery
Ali, Anum
2016-09-29
This paper presents a novel narrowband interference (NBI) mitigation scheme for single carrier-frequency division multiple access systems. The proposed NBI cancellation scheme exploits the frequency-domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen data locations are kept data free to sense the NBI signal at the receiver. Furthermore, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between the NBI sources and the system under consideration. Toward this end, first, an accurate grid mismatch model is presented that is capable of assuming independent offsets for multiple NBI sources, and second, the sparsity of the unknown signal is restored prior to reconstruction using a sparsifying transform. To improve the spectral efficiency of the proposed scheme, a data-aided NBI recovery procedure is outlined that relies on adaptively selecting a subset of data-points and using them as additional measurements. Numerical results demonstrate the effectiveness of the proposed scheme for NBI mitigation.
Bayesian approach for the reliability assessment of corroded interdependent pipe networks
International Nuclear Information System (INIS)
Ait Mokhtar, El Hassene; Chateauneuf, Alaa; Laggoune, Radouane
2016-01-01
Pipelines under corrosion are subject to various environment conditions, and consequently it becomes difficult to build realistic corrosion models. In the present work, a Bayesian methodology is proposed to allow for updating the corrosion model parameters according to the evolution of environmental conditions. For reliability assessment of dependent structures, Bayesian networks are used to provide interesting qualitative and quantitative description of the information in the system. The qualitative contribution lies in the modeling of complex system, composed by dependent pipelines, as a Bayesian network. The quantitative one lies in the evaluation of the dependencies between pipelines by the use of a new method for the generation of conditional probability tables. The effectiveness of Bayesian updating is illustrated through an application where the new reliability of degraded (corroded) pipe networks is assessed. - Highlights: • A methodology for Bayesian network modeling of pipe networks is proposed. • Bayesian approach based on Metropolis - Hastings algorithm is conducted for corrosion model updating. • The reliability of corroded pipe network is assessed by considering the interdependencies between the pipelines.
Directory of Open Access Journals (Sweden)
André Chiaradia
Full Text Available Reconstructing the diet of top marine predators is of great significance in several key areas of applied ecology, requiring accurate estimation of their true diet. However, from conventional stomach content analysis to recent stable isotope and DNA analyses, no one method is bias or error free. Here, we evaluated the accuracy of recent methods to estimate the actual proportion of a controlled diet fed to a top-predator seabird, the Little penguin (Eudyptula minor. We combined published DNA data of penguins scats with blood plasma δ(15N and δ(13C values to reconstruct the diet of individual penguins fed experimentally. Mismatch between controlled (true ingested diet and dietary estimates obtained through the separately use of stable isotope and DNA data suggested some degree of differences in prey assimilation (stable isotope and digestion rates (DNA analysis. In contrast, combined posterior isotope mixing model with DNA Bayesian priors provided the closest match to the true diet. We provided the first evidence suggesting that the combined use of these complementary techniques may provide better estimates of the actual diet of top marine predators- a powerful tool in applied ecology in the search for the true consumed diet.
Long-term Outcomes After Flap Reconstruction in Pediatric Pressure Ulcers.
Firriolo, Joseph M; Ganske, Ingrid M; Pike, Carolyn M; Caillouette, Catherine; Faulkner, Heather R; Upton, Joseph; Labow, Brian I
2018-02-01
Pressure ulcers refractory to nonoperative management may undergo flap reconstruction. This study aims to evaluate the long-term outcomes and recurrence rates of flap reconstruction for pediatric pressure ulcers. We reviewed the records of patients who underwent flap reconstruction for pressure ulcer(s) from 1995 to 2013. Twenty-four patients with 30 pressure ulcers, requiring 52 flaps were included. Ulcers were stages III and IV and mostly involved either the ischia (15/30) or sacrum (8/30). Flaps were followed for a median of 4.9 years. Twenty-three patients were wheelchair dependent, and 20 had sensory impairment at their ulcer site(s). Ten patients had a history of noncompliance with preoperative management, 8 of whom experienced ulcer recurrence. Twenty-one ulcers had underlying osteomyelitis, associated with increased admissions (P = 0.019) and cumulative length of stay (P = 0.031). Overall, there was a 42% recurrence rate in ulceration after flap reconstruction. Recurrence was associated with a preoperative history of noncompliance with nonoperative therapy (P = 0.030), but not with flap type or location, age, sex, body mass index, osteomyelitis, or urinary/fecal incontinence (P > 0.05, all). Flap reconstruction can be beneficial in the management of pediatric pressure ulcers. Although high rates of long-term success with this intervention have been reported in children, we found rates of ulcer recurrence similar to that seen in adults. Poor compliance with nonoperative care and failure to modify the biopsychosocial perpetuators of pressure ulcers will likely eventuate in postoperative recurrence. Despite the many comorbidities observed in our patient sample, compliance was the best indicator of long-term skin integrity and flap success.
Bayesian target tracking based on particle filter
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
Paleo-Environmental Reconstruction Using Ancient DNA
DEFF Research Database (Denmark)
Pedersen, Mikkel Winther
The aim of this thesis has been to investigate and expand the methodology and applicability for using ancient DNA deposited in lake sediments to detect and determine its genetic sources for paleo-environmental reconstruction. The aim was furthermore to put this tool into an applicable context...... solving other scientifically interesting questions. Still in its childhood, ancient environmental DNA research has a large potential for still developing, improving and discovering its possibilities and limitations in different environments and for identifying various organisms, both in terms...... research on ancient and modern environmental DNA (Paper 1), secondly by setting up a comparative study (Paper 2) to investigate how an ancient plant DNA (mini)-barcode can reflect other traditional methods (e.g. pollen and macrofossils) for reconstructing floristic history. In prolongation of the results...
Pieces of a thousand stories: repatriation of the history of Aboriginal Sydney
Directory of Open Access Journals (Sweden)
Peter Read
2010-09-01
Full Text Available The on-line project A History of Aboriginal Sydney, based at the University of Sydney, takes existing educational and Australian Indigenous digital initiatives in a new direction. By dividing Sydney into six geographical areas, we are creating a knowledge base of post-invasion Aboriginal history, incorporating different forms of tagging, timeline and digital mapping to provide multiple paths to information in text, videos, still images and, in the future, three dimensional reconstructions of former living areas. After eighteen months research we are maintaining a balance between unearthing new and forgotten material, incorporating it into our developing database, and exploring the potential of digital mapping, animation and 3D historical reconstruction for educational and research purposes. With close Indigenous consultation, especially the Aboriginal Educational Consultative Groups, we hope to digitally construct the Aboriginal history of Sydney and return it to the people who have been deprived of so much of their history for so long.
Bayesian statistical inference
Directory of Open Access Journals (Sweden)
Bruno De Finetti
2017-04-01
Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.
Bayesian Exponential Smoothing.
Forbes, C.S.; Snyder, R.D.; Shami, R.S.
2000-01-01
In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.
The phylogeny and evolutionary history of tyrannosauroid dinosaurs
Brusatte, Stephen L.; Carr, Thomas D.
2016-02-01
Tyrannosauroids—the group of carnivores including Tyrannosaurs rex—are some of the most familiar dinosaurs of all. A surge of recent discoveries has helped clarify some aspects of their evolution, but competing phylogenetic hypotheses raise questions about their relationships, biogeography, and fossil record quality. We present a new phylogenetic dataset, which merges published datasets and incorporates recently discovered taxa. We analyze it with parsimony and, for the first time for a tyrannosauroid dataset, Bayesian techniques. The parsimony and Bayesian results are highly congruent, and provide a framework for interpreting the biogeography and evolutionary history of tyrannosauroids. Our phylogenies illustrate that the body plan of the colossal species evolved piecemeal, imply no clear division between northern and southern species in western North America as had been argued, and suggest that T. rex may have been an Asian migrant to North America. Over-reliance on cranial shape characters may explain why published parsimony studies have diverged and filling three major gaps in the fossil record holds the most promise for future work.
The phylogeny and evolutionary history of tyrannosauroid dinosaurs.
Brusatte, Stephen L; Carr, Thomas D
2016-02-02
Tyrannosauroids--the group of carnivores including Tyrannosaurs rex--are some of the most familiar dinosaurs of all. A surge of recent discoveries has helped clarify some aspects of their evolution, but competing phylogenetic hypotheses raise questions about their relationships, biogeography, and fossil record quality. We present a new phylogenetic dataset, which merges published datasets and incorporates recently discovered taxa. We analyze it with parsimony and, for the first time for a tyrannosauroid dataset, Bayesian techniques. The parsimony and Bayesian results are highly congruent, and provide a framework for interpreting the biogeography and evolutionary history of tyrannosauroids. Our phylogenies illustrate that the body plan of the colossal species evolved piecemeal, imply no clear division between northern and southern species in western North America as had been argued, and suggest that T. rex may have been an Asian migrant to North America. Over-reliance on cranial shape characters may explain why published parsimony studies have diverged and filling three major gaps in the fossil record holds the most promise for future work.
The Actuality of Gentile's Philosophy of History
Peters, Richard
2014-01-01
This essay reconstructs Gentile's conception of history as the product of the eternal act of thinking. Peters charts the development of this distinctive position, presenting it as a sustained attempt to unite past and present, fact and value, thought and action within a single theory. He argues,
The Actuality of Gentile's Philosophy of History
Peters, Richard; Haddock, Bruce; Wakefield, James
2015-01-01
This essay reconstructs Gentile's conception of history as the product of the eternal act of thinking. Peters charts the development of this distinctive position, presenting it as a sustained attempt to unite past and present, fact and value, thought and action within a single theory. He argues,
Power in Bayesian Mediation Analysis for Small Sample Research
Miočević, M.; MacKinnon, David; Levy, Roy
2017-01-01
Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product,
Bayesian analyses of seasonal runoff forecasts
Krzysztofowicz, R.; Reese, S.
1991-12-01
Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.
Bayesian methodology for reliability model acceptance
International Nuclear Information System (INIS)
Zhang Ruoxue; Mahadevan, Sankaran
2003-01-01
This paper develops a methodology to assess the reliability computation model validity using the concept of Bayesian hypothesis testing, by comparing the model prediction and experimental observation, when there is only one computational model available to evaluate system behavior. Time-independent and time-dependent problems are investigated, with consideration of both cases: with and without statistical uncertainty in the model. The case of time-independent failure probability prediction with no statistical uncertainty is a straightforward application of Bayesian hypothesis testing. However, for the life prediction (time-dependent reliability) problem, a new methodology is developed in this paper to make the same Bayesian hypothesis testing concept applicable. With the existence of statistical uncertainty in the model, in addition to the application of a predictor estimator of the Bayes factor, the uncertainty in the Bayes factor is explicitly quantified through treating it as a random variable and calculating the probability that it exceeds a specified value. The developed method provides a rational criterion to decision-makers for the acceptance or rejection of the computational model
Surreal aroma's. (Reconstructing the volatile heritage of Marcel Duchamp
Directory of Open Access Journals (Sweden)
Caro Verbeek
2016-06-01
Full Text Available No ‘visual’ artist addressed the sense of smell as often as Marcel Duchamp did. Whereas his solid objects can still be studied visually and textually, the scents he used have by now evaporated, and a vocabulary to describe them is lacking until today. What we have left are nose witness reports and the possibility to smell olfactory reconstructions. Rereading canonical text with a more sensory gaze and inhaling these historical fragrances, such as cedar, erotic perfumes and coffee, will enable us to reconstruct the olfactory dimension of our highly ocularcentric history of art.
Li, L.; Xu, C.-Y.; Engeland, K.
2012-04-01
With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD
Ducrot, Virginie; Billoir, Elise; Péry, Alexandre R R; Garric, Jeanne; Charles, Sandrine
2010-05-01
Effects of zinc were studied in the freshwater worm Branchiura sowerbyi using partial and full life-cycle tests. Only newborn and juveniles were sensitive to zinc, displaying effects on survival, growth, and age at first brood at environmentally relevant concentrations. Threshold effect models were proposed to assess toxic effects on individuals. They were fitted to life-cycle test data using Bayesian inference and adequately described life-history trait data in exposed organisms. The daily asymptotic growth rate of theoretical populations was then simulated with a matrix population model, based upon individual-level outputs. Population-level outputs were in accordance with existing literature for controls. Working in a Bayesian framework allowed incorporating parameter uncertainty in the simulation of the population-level response to zinc exposure, thus increasing the relevance of test results in the context of ecological risk assessment.