OFDM receiver for fast time-varying channels using block-sparse Bayesian learning
DEFF Research Database (Denmark)
Barbu, Oana-Elena; Manchón, Carles Navarro; Rom, Christian;
2016-01-01
characterized with a basis expansion model using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning. Using variational Bayesian...... inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference cancellation, detection and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-art receivers.......We propose an iterative algorithm for OFDM receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few non-negligible separable multipath components, and the temporal variation of each component gain can be well...
A Bayesian Networks approach to Operational Risk
Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; Ferri, G.
2010-04-01
A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.
Andrew Sanford; Imad Moosa
2015-01-01
This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at-Risk, for a structured finance operations unit located within one of Australia's major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited ava...
Bayesian Methods for Measuring Operational Risk
Carol Alexander
2000-01-01
The likely imposition by regulators of minimum standards for capital to cover 'other risks' has been a driving force behind the recent interest in operational risk management. Much discussion has been centered on the form of capital charges for other risks. At the same time major banks are developing models to improve internal management of operational processes, new insurance products for operational risks are being designed and there is growing interest in alternative risk transfer, through...
Modeling operational risks of the nuclear industry with Bayesian networks
International Nuclear Information System (INIS)
Basically, planning a new industrial plant requires information on the industrial management, regulations, site selection, definition of initial and planned capacity, and on the estimation of the potential demand. However, this is far from enough to assure the success of an industrial enterprise. Unexpected and extremely damaging events may occur that deviates from the original plan. The so-called operational risks are not only in the system, equipment, process or human (technical or managerial) failures. They are also in intentional events such as frauds and sabotage, or extreme events like terrorist attacks or radiological accidents and even on public reaction to perceived environmental or future generation impacts. For the nuclear industry, it is a challenge to identify and to assess the operational risks and their various sources. Early identification of operational risks can help in preparing contingency plans, to delay the decision to invest or to approve a project that can, at an extreme, affect the public perception of the nuclear energy. A major problem in modeling operational risk losses is the lack of internal data that are essential, for example, to apply the loss distribution approach. As an alternative, methods that consider qualitative and subjective information can be applied, for example, fuzzy logic, neural networks, system dynamic or Bayesian networks. An advantage of applying Bayesian networks to model operational risk is the possibility to include expert opinions and variables of interest, to structure the model via causal dependencies among these variables, and to specify subjective prior and conditional probabilities distributions at each step or network node. This paper suggests a classification of operational risks in industry and discusses the benefits and obstacles of the Bayesian networks approach to model those risks. (author)
Interpretation of Receiver Operating Characteristics (ROC)
International Nuclear Information System (INIS)
The purpose of this paper is to explain the making procedure and the usage of receiver operating characteristic curve (ROC) for interpretation of radiographic images. The conventional radiograms obtained after the creation of the lesions in the acrylic plates and were enhanced in color. The observer were informed of which tooth to examine, the 'a priori' probability of a lesion present and the approximate diameter of the lesions. The two groups of films were interpreted separately by the same observer using the same rating scale. The following rating scale was used: A; definitely no lesion, B; probably no lesion, C; not sure, D; probably a lesion, and E; definitely a lesion. In analysis, for each observer the diagnostic results in terms of true positive (TP) and false positive (FP) decisions were plotted on a graph. The lowest point on the graph represents the TP and FP when only decisions designated as E according to the rating scale are included. The next point shows the TP and FP values when diagnoses designated as D are added and so forth. By connecting such plot points, a receiver operating characteristic curves (ROC) is obtained. The area under the curve represents the diagnostic accuracy resulting from a diagnostic performance at pure chance level and a value of 1.0 at perfect performance. This method has been known as an useful method to detect the minute difference for each radiographic technic, each observer and for the different lesion depths.
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
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.
Cost Analysis of different Operation strategies for falling particle receivers
Gobereit, Birgit; Amsbeck, Lars; Buck, Reiner; Singer, Csaba
2015-01-01
The potential for highly efficient and cost competitive solar energy collection at high temperatures drives the actual research and development activities for particle tower systems. One promising concept for particle receivers is the falling particle receiver. This paper is related to a particle receiver, in which falling ceramic particles form a particle curtain, which absorbs the concentrated solar radiation. Complex Operation strategies will result in higher receiver costs, for both...
Risk-Based Operation and Maintenance Using Bayesian Networks
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2011-01-01
This paper describes how risk-based decision making can be used for maintenance planning of components exposed to degradation such as fatigue in offshore wind turbines. In fatigue models, large epistemic uncertainties are usually present. These can be reduced if monitoring results are used to...... update the models, and hereby a better basis for decision making is obtained. An application example shows how a Bayesian network model can be used as a tool for updating the model and assist in risk-based decision making....
Modelling macroeconomic e ects and expert judgements in operational risk : a Bayesian approach
Capa Santos, Holger; Kratz, Marie; Mosquera Munoz, Franklin
2012-01-01
This work presents a contribution on operational risk under a general Bayesian context incorporating information on market risk pro le, experts and operational losses, taking into account the general macroeconomic environment as well. It aims at estimating a characteristic parameter of the distributions of the sources, market risk pro le, experts and operational losses, chosen here at a location parameter. It generalizes under more realistic conditions a study realized by Lambrigger, Shevchen...
Risk-Based Operation and Maintenance of Offshore Wind Turbines using Bayesian Networks
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2011-01-01
For offshore wind farms, the costs due to operation and maintenance are large, and more optimal planning has the potential of reducing these costs. This paper presents how Bayesian networks can be used for risk-based inspection planning, where the inspection plans are updated each year through the...
Application of Bayesian networks for risk analysis of MV air insulated switch operation
International Nuclear Information System (INIS)
Electricity distribution companies regard risk-based approaches as a good philosophy to address their asset management challenges, and there is an increasing trend on developing methods to support decisions where different aspects of risks are taken into consideration. This paper describes a methodology for application of Bayesian networks for risk analysis in electricity distribution system maintenance management. The methodology is used on a case analysing safety risk related to operation of MV air insulated switches. The paper summarises some challenges and benefits of using Bayesian networks as a part of distribution system maintenance management.
Kim, Seongryong; Dettmer, Jan; Rhie, Junkee; Tkalčić, Hrvoje
2016-04-01
With the deployment of extensive seismic arrays, systematic and efficient parameter and uncertainty estimation is of increasing importance and can provide reliable, regional models for crustal and upper-mantle structure. We present an efficient Bayesian method for the joint inversion of surface-wave dispersion and receiver-function data that combines trans-dimensional (trans-D) model selection in an optimisation phase with subsequent rigorous parameter uncertainty estimation. Parameter and uncertainty estimation depend strongly on the chosen parametrization such that meaningful regional comparison requires quantitative model selection that can be carried out efficiently at several sites. While significant progress has been made for model selection (e.g. trans-D inference) at individual sites, the lack of efficiency can prohibit application to large data volumes or cause questionable results due to lack of convergence. Studies that address large numbers of data sets have mostly ignored model selection in favour of more efficient/simple estimation techniques (i.e. focusing on uncertainty estimation but employing ad-hoc model choices). Our approach consists of a two-phase inversion that combines trans-D optimisation to select the most probable parametrization with subsequent Bayesian sampling for uncertainty estimation given that parametrization. The trans-D optimisation is implemented here by replacing the likelihood function with the Bayesian information criterion (BIC). The BIC provides constraints on model complexity that facilitate the search for an optimal parametrization. Parallel tempering (PT) is applied as an optimisation algorithm. After optimisation, the optimal model choice is identified by the minimum BIC value from all PT chains. Uncertainty estimation is then carried out in fixed dimension. Data errors are estimated as part of the inference problem by a combination of empirical and hierarchical estimation. Data covariance matrices are estimated from
Dynamic Bayesian modeling for risk prediction in credit operations
DEFF Research Database (Denmark)
Borchani, Hanen; Fernandez, Ana Maria Martinez; Masegosa, Andres;
2015-01-01
Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context of...
Planetary micro-rover operations on Mars using a Bayesian framework for inference and control
Post, Mark A.; Li, Junquan; Quine, Brendan M.
2016-03-01
With the recent progress toward the application of commercially-available hardware to small-scale space missions, it is now becoming feasible for groups of small, efficient robots based on low-power embedded hardware to perform simple tasks on other planets in the place of large-scale, heavy and expensive robots. In this paper, we describe design and programming of the Beaver micro-rover developed for Northern Light, a Canadian initiative to send a small lander and rover to Mars to study the Martian surface and subsurface. For a small, hardware-limited rover to handle an uncertain and mostly unknown environment without constant management by human operators, we use a Bayesian network of discrete random variables as an abstraction of expert knowledge about the rover and its environment, and inference operations for control. A framework for efficient construction and inference into a Bayesian network using only the C language and fixed-point mathematics on embedded hardware has been developed for the Beaver to make intelligent decisions with minimal sensor data. We study the performance of the Beaver as it probabilistically maps a simple outdoor environment with sensor models that include uncertainty. Results indicate that the Beaver and other small and simple robotic platforms can make use of a Bayesian network to make intelligent decisions in uncertain planetary environments.
International Nuclear Information System (INIS)
In the past decades, engineering systems become more and more complex, and generally work at different operational modes. Since incipient fault can lead to dangerous accidents, it is crucial to develop strategies for online operational safety assessment. However, the existing online assessment methods for multi-mode engineering systems commonly assume that samples are independent, which do not hold for practical cases. This paper proposes a probabilistic framework of online operational safety assessment of multi-mode engineering systems with sample dependency. To begin with, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Then, based on the definition of safety index (SI), the SI for one single mode is calculated. At last, the Bayesian method is presented to calculate the posterior probabilities belonging to each operating mode with sample dependency. The proposed assessment strategy is applied in two examples: one is the aircraft gas turbine, another is an industrial dryer. Both examples illustrate the efficiency of the proposed method
Receiver Operating Characteristic Analysis for Detecting Explosives-related Threats
Energy Technology Data Exchange (ETDEWEB)
Oxley, Mark E; Venzin, Alexander M
2012-11-14
The Department of Homeland Security (DHS) and the Transportation Security Administration (TSA) are interested in developing a standardized testing procedure for determining the performance of candidate detection systems. This document outlines a potential method for judging detection system performance as well as determining if combining the information from a legacy system with a new system can signicantly improve performance. In this document, performance corresponds to the Neyman-Pearson criterion applied to the Receiver Operating Characteristic (ROC) curves of the detection systems in question. A simulation was developed to investigate how the amount of data provided by the vendor in the form of the ROC curve eects the performance of the combined detection system. Furthermore, the simulation also takes into account the potential eects of correlation and how this information can also impact the performance of the combined system.
Receiver operating characteristic analysis under tree orderings of disease classes.
Wang, Dan; Attwood, Kristopher; Tian, Lili
2016-05-20
Receiver operating characteristic (ROC) curve and its summary statistics (e.g., the area under curve (AUC)) are commonly used to evaluate the diagnostic accuracy for disease processes with binary classification. The ROC curve has been extended to ROC surface for scenarios with three ordinal classes or to hyper-surface for scenarios with more than three classes. For classifier under tree or umbrella ordering in which the marker measurement for one class is lower or higher than those for the other classes, the commonly adopted diagnostic measures are the naive AUC (NAUC) based on a pooled class of all the unordered classes and the umbrella volume (UV) based on the concept of volume under surface. However, both NAUC and UV have some limitations. For example, NAUC depends on the sampling weights for all the classes in population, and UV has only been introduced for three-class settings. In this article, we initiate the idea of a new ROC framework for tree or umbrella ordering (denoted as TROC) and propose the area under TROC curve (denoted as TAUC) as an appropriate diagnostic measure. The proposed TROC and TAUC share many nice features with the traditional ROC and AUC. Both parametric and nonparametric approaches are explored to construct the confidence interval estimation of TAUC. The performances of these methods are compared in simulation studies under a variety settings. At the end, the proposed methods are applied to a published microarray data set. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26678355
Analysis of shoulder MR imaging using receiver operating characteristic curve
International Nuclear Information System (INIS)
The purpose of this study is to assess the utility of shoulder MRI by using ROC (receiver operating characteristic curve in the evaluation of rotator cuff tear, anterior labral tear, superior labral tear and hill-sachs lesion. We evaluated 38 arthroscopically confirmed patients who had undergone shoulder MRI. According to the signal intensity of the rotator cuff, as seen on T2 and proton density imaging, a five-stage grading system was devised. Labral tears were graded according to the separation of the labium; this was based on gleaned, morphologic and signal intensity changes of the labrum : six grades of anterior labral tear and three grades of superior labral tear. Hill-sachs lesion was also classified into four grades according to morphologic and signal changes of the humeral head. These findings were reviewed by two musculoskeletal radiologists and ROC curves and areas under the curve (Az) was obtained. The accuracy of shoulder MRI using ROC curve was relatively high in rotator cuff tear, anterior labral tear and Hill-sachs lesion, but low in superior labral tear. (author). 40 refs., 1 tab., 6 figs
Receiver operating characteristic analysis improves diagnosis by radionuclide ventriculography
International Nuclear Information System (INIS)
Receiver operating characteristic analysis (ROC) evaluates continuous variables to define diagnostic criteria for the optimal sensitivity (SENS) and specificity (SPEC) of a test. The authors studied exercise-induced chest pain (CP), ST-changes on electrocardiography (ECG) and rest-exercise gated radionuclide ventriculography (RVG) using ROC to clarify the optimal criteria for detecting myocardial ischemia due to coronary artherosclerosis (CAD). The data of 95 consecutive patients studied with coronary angiography, rest-exercise RVG and ECG were reviewed. 77 patients had ''significant'' CAD (≥50% lesions). Exercise-induced CP, ECG abnormalities (ST-T shifts) and RVG abnormalities (change in ejection fraction, 2-view regional wall motion change and relative end-systolic volume) were evaluated to define optimal SENS/SPEC of each and for the combined data. ROC curves were constructed by multiple logistic regression (MLR). By MLR, RVG alone was superior to ECG and CP. The combination of all three produced the best ROC curve for the entire group and for clinical subsets based on the number of diseased vessels and the presence or absence of prior myocardial infarction. When CP, ECG and RVG were combined, the optimal SENS/SPEC for detection of single vessel disease was 88/86. The SENS/SPEC for 3 vessel disease was 93/95. Thus, the application of RVG for the diagnosis of myocardial ischemia is improved with the inclusion of ECG and CP data by the use of a multiple logistic regression model. ROC analysis allows clinical application of multiple data for diagnosing CAD at desired SENS/SPEC rather than by arbitrary single-standard criteria
Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior
Zaientz, Jack D.; DeKoven, Elyon; Piegdon, Nicholas; Wood, Scott D.; Huber, Marcus J.
2006-05-01
Trends in combat technology research point to an increasing role for uninhabited vehicles in modern warfare tactics. To support increased span of control over these vehicles human responsibilities need to be transformed from tedious, error-prone and cognition intensive operations into tasks that are more supervisory and manageable, even under intensely stressful conditions. The goal is to move away from only supporting human command of low-level system functions to intention-level human-system dialogue about the operator's tasks and situation. A critical element of this process is developing the means to identify when human operators need automated assistance and to identify what assistance they need. Toward this goal, we are developing an unmanned vehicle operator task recognition system that combines work in human behavior modeling and Bayesian plan recognition. Traditionally, human behavior models have been considered generative, meaning they describe all possible valid behaviors. Basing behavior recognition on models designed for behavior generation can offers advantages in improved model fidelity and reuse. It is not clear, however, how to reconcile the structural differences between behavior recognition and behavior modeling approaches. Our current work demonstrates that by pairing a cognitive psychology derived human behavior modeling approach, GOMS, with a Bayesian plan recognition engine, ASPRN, we can translate a behavior generation model into a recognition model. We will discuss the implications for using human performance models in this manner as well as suggest how this kind of modeling may be used to support the real-time control of multiple, uninhabited battlefield vehicles and other semi-autonomous systems.
International Nuclear Information System (INIS)
During the last three decades, several techniques have been developed for the quantitative study of human reliability. In the 1980s, techniques were developed to model systems by means of binary trees, which did not allow for the representation of the context in which human actions occur. Thus, these techniques cannot model the representation of individuals, their interrelationships, and the dynamics of a system. These issues make the improvement of methods for Human Reliability Analysis (HRA) a pressing need. To eliminate or at least attenuate these limitations, some authors have proposed modeling systems using Bayesian Belief Networks (BBNs). The application of these tools is expected to address many of the deficiencies in current approaches to modeling human actions with binary trees. This paper presents a methodology based on BBN for analyzing human reliability and applies this method to the operation of an oil tanker, focusing on the risk of collision accidents. The obtained model was used to determine the most likely sequence of hazardous events and thus isolate critical activities in the operation of the ship to study Internal Factors (IFs), Skills, and Management and Organizational Factors (MOFs) that should receive more attention for risk reduction.
Posterior consistency and convergence rates for Bayesian inversion with hypoelliptic operators
Kekkonen, Hanne; Lassas, Matti; Siltanen, Samuli
2016-08-01
The Bayesian approach to inverse problems is studied in the case where the forward map is a linear hypoelliptic pseudodifferential operator and measurement error is additive white Gaussian noise. The measurement model for an unknown Gaussian random variable U(x,ω ) is {M}δ (y,ω )=A (U(x,ω ))+δ \\phantom{\\rule{.2mm}{0ex}}{ E }(y,ω ), where A is a finitely many orders smoothing linear hypoelliptic operator and δ \\gt 0 is the noise magnitude. The covariance operator C U of U is smoothing of order 2r, self-adjoint, injective and elliptic pseudodifferential operator. If { E } was taking values in L 2 then in Gaussian case solving the conditional mean (and maximum a posteriori) estimate is linked to solving the minimisation problem {T}δ ({m}δ )={{arg}{min}}u\\in {Hr} \\{\\parallel {Au}-{m}δ {\\parallel }{L2}2+{δ }2\\parallel {C}U-1/2u{\\parallel }{L2}2\\}. However, Gaussian white noise does not take values in L 2 but in {H}-s where s\\gt 0 is big enough. A modification of the above approach to solve the inverse problem is presented, covering the case of white Gaussian measurement noise. Furthermore, the convergence of the conditional mean estimate to the correct solution as δ \\to 0 is proven in appropriate function spaces using microlocal analysis. Also the frequentist posterior contractions rates are studied.
A Bayesian view of the Higgs sector with higher dimensional operators
Dumont, Beranger; von Gersdorff, Gero
2013-01-01
We investigate the possibilities of New Physics affecting the Standard Model (SM) Higgs sector. An effective Lagrangian with dimension-six operators is used to capture the effect of New Physics. We carry out a global Bayesian inference analysis, considering the recent LHC data set including all available correlations, as well as results from Tevatron. Trilinear gauge boson couplings and electroweak precision observables are also taken into account. The case of weak bosons tensorial couplings is closely examined and NLO QCD corrections are taken into account in the deviations we predict. We consider two scenarios, one where the coefficients of all the dimension-six operators are essentially unconstrained, and one where a certain subset is loop suppressed. In both scenarios, we find that large deviations from some of the SM Higgs couplings can still be present, assuming New Physics arising at 3 TeV. In particular, we find that a significantly reduced coupling of the Higgs to the top quark is possible and slight...
Di Troia, C.
2015-11-01
A class of parametric distribution functions was proposed in (Di Troia 2012 Plasma Phys. Control. Fusion 54 105017) as equilibrium distribution functions (EDFs) for charged particles in fusion plasmas, representing supra-thermal particles in anisotropic equilibria for Neutral Beam Injection and Ion Cyclotron Heating scenarios. Moreover, those EDFs can be used to represent also nearly isotropic equilibria for Slowing-Down alpha particles and core thermal plasma populations. Such EDFs depend on constants of motion (COMs). In axisymmetric system with no equilibrium electric field, they depend on toroidal canonical momentum {{P}φ} , kinetic energy w and magnetic moment μ. In the present work, the same EDFs are obtained from first principles and general hypothesis. The derivation is probabilistic and makes use of the Bayes’ Theorem. The bayesian argument is used to describe how the plasma is far from the prior probability distribution function (pdf), e.g. Maxwellian, based on the information obtained from magnetic moment and guiding center velocity pdf. Once the general functional form of the EDF has been settled, it is shown how to associate a modified Landau collision operator in the Fokker-Planck equation, to describe the system relaxation towards the proposed EDF.
Energy Technology Data Exchange (ETDEWEB)
Kang, Seongkeun; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)
2014-05-15
The purpose of this paper is to confirm if Bayesian inference can properly reflect the situation awareness of real human operators, and find the difference between the situation of ideal and practical operators, and investigate the factors which contributes to those difference. As a results, human can not think like computer. If human can memorize all the information, and their thinking process is same to the CPU of computer, the results of these two experiments come out more than 99%. However the probability of finding right malfunction by humans are only 64.52% in simple experiment, and 51.61% in complex experiment. Cognition is the mental processing that includes the attention of working memory, comprehending and producing language, calculating, reasoning, problem solving, and decision making. There are many reasons why human thinking process is different with computer, but in this experiment, we suggest that the working memory is the most important factor. Humans have limited working memory which has only seven chunks capacity. These seven chunks are called magic number. If there are more than seven sequential information, people start to forget the previous information because their working memory capacity is running over. We can check how much working memory affects to the result through the simple experiment. Then what if we neglect the effect of working memory? The total number of subjects who have incorrect memory is 7 (subject 3, 5, 6, 7, 8, 15, 25). They could find the right malfunction if the memory hadn't changed because of lack of working memory. Then the probability of find correct malfunction will be increased to 87.10% from 64.52%. Complex experiment has similar result. In this case, eight subjects(1, 5, 8, 9, 15, 17, 18, 30) had changed the memory, and it affects to find the right malfunction. Considering it, then the probability would be (16+8)/31 = 77.42%.
International Nuclear Information System (INIS)
The purpose of this paper is to confirm if Bayesian inference can properly reflect the situation awareness of real human operators, and find the difference between the situation of ideal and practical operators, and investigate the factors which contributes to those difference. As a results, human can not think like computer. If human can memorize all the information, and their thinking process is same to the CPU of computer, the results of these two experiments come out more than 99%. However the probability of finding right malfunction by humans are only 64.52% in simple experiment, and 51.61% in complex experiment. Cognition is the mental processing that includes the attention of working memory, comprehending and producing language, calculating, reasoning, problem solving, and decision making. There are many reasons why human thinking process is different with computer, but in this experiment, we suggest that the working memory is the most important factor. Humans have limited working memory which has only seven chunks capacity. These seven chunks are called magic number. If there are more than seven sequential information, people start to forget the previous information because their working memory capacity is running over. We can check how much working memory affects to the result through the simple experiment. Then what if we neglect the effect of working memory? The total number of subjects who have incorrect memory is 7 (subject 3, 5, 6, 7, 8, 15, 25). They could find the right malfunction if the memory hadn't changed because of lack of working memory. Then the probability of find correct malfunction will be increased to 87.10% from 64.52%. Complex experiment has similar result. In this case, eight subjects(1, 5, 8, 9, 15, 17, 18, 30) had changed the memory, and it affects to find the right malfunction. Considering it, then the probability would be (16+8)/31 = 77.42%
Ticlavilca, Andres M.
2010-01-01
The principal objective of this dissertation is to develop Bayesian machine learning models for multiple reservoir, irrigation canal, and river system operation and management. These types of models are derived from the emerging area of machine learning theory; they are characterized by their ability to capture the underlying physics of the system simply by examination of the measured system inputs and outputs. They can be used to provide probabilistic predictions of system behavior using onl...
Toroody, Ahmad Bahoo; Abaiee, Mohammad Mahdi; Gholamnia, Reza; Ketabdari, Mohammad Javad
2016-07-01
Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.
Kim, Seongryong; Dettmer, Jan; Rhie, Junkee; Tkalčić, Hrvoje
2016-07-01
With the deployment of extensive seismic arrays, systematic and efficient parameter and uncertainty estimation is of increasing importance and can provide reliable, regional models for crustal and upper-mantle structure. We present an efficient Bayesian method for the joint inversion of surface-wave dispersion and receiver-function data that combines trans-dimensional (trans-D) model selection in an optimization phase with subsequent rigorous parameter uncertainty estimation. Parameter and uncertainty estimation depend strongly on the chosen parametrization such that meaningful regional comparison requires quantitative model selection that can be carried out efficiently at several sites. While significant progress has been made for model selection (e.g. trans-D inference) at individual sites, the lack of efficiency can prohibit application to large data volumes or cause questionable results due to lack of convergence. Studies that address large numbers of data sets have mostly ignored model selection in favour of more efficient/simple estimation techniques (i.e. focusing on uncertainty estimation but employing ad-hoc model choices). Our approach consists of a two-phase inversion that combines trans-D optimization to select the most probable parametrization with subsequent Bayesian sampling for uncertainty estimation given that parametrization. The trans-D optimization is implemented here by replacing the likelihood function with the Bayesian information criterion (BIC). The BIC provides constraints on model complexity that facilitate the search for an optimal parametrization. Parallel tempering (PT) is applied as an optimization algorithm. After optimization, the optimal model choice is identified by the minimum BIC value from all PT chains. Uncertainty estimation is then carried out in fixed dimension. Data errors are estimated as part of the inference problem by a combination of empirical and hierarchical estimation. Data covariance matrices are estimated from
International Nuclear Information System (INIS)
The operating characteristics of a repository receiving facility structured around current technology and practices have been reviewed. Cask turnaround times and operator doses were estimated. Large throughout and long-term receiving operations at a nuclear waste repository result in an unprecedented number of casks being handled. While the current generation of material-handling equipment is adequate to process the casks, personnel radiation exposures for the generic facility analyzed are unacceptably high. This emphasizes the need for development of occupational radiation exposure control concepts for application in repository receiving facilities. 3 refs., 22 figs., 6 tabs
Correlator receiver architecture with PnpN optical thyristor operating as optical hard-limiter
Kang, Tae-Gu; Ho Lee, Su; Park, Soonchul
2011-07-01
We propose novel correlator receiver architecture with a PnpN optical thyristor operating as optical hard-limiter, and demonstrate a multiple-access interference rejection of the proposed correlator receiver. The proposed correlator receiver is composed of the 1×2 splitter, optical delay line, 2×1 combiner, and fabricated PnpN optical thyristor. The proposed correlator receiver enhances the system performance because it excludes some combinations of multiple-access interference patterns from causing errors as in optical code-division multiple access systems with conventional optical receiver shown in all previous works. It is found that the proposed correlator receiver can fully reject the interference signals generated by decoding processing and multiple access for two simultaneous users.
Energy Technology Data Exchange (ETDEWEB)
Kang, Seong Keun; Seong, Poong Hyun [KAIST, Daejeon (Korea, Republic of)
2014-08-15
Bayesian methodology has been used widely used in various research fields. It is method of inference using Bayes' rule to update the estimation of probability for the certain hypothesis when additional evidences are acquired. According to the current researches, malfunction of nuclear power plant can be detected by using this Bayesian inference which consistently piles up the newly incoming data and updates its estimation. However, those researches are based on the assumption that people are doing like computer perfectly, which can be criticized and may cause a problem in real world application. Studies in cognitive psychology indicates that when the amount of information becomes larger, people can't save the whole data because people have limited memory capacity which is well known as working memory, and also they have attention problem. The purpose of this paper is to consider the psychological factors and confirm how much this working memory and attention will affect the resulted estimation based on the Bayesian inference. To confirm this, experiment on human is needed, and the tool of experiment is Compact Nuclear Simulator (CNS)
International Nuclear Information System (INIS)
Bayesian methodology has been used widely used in various research fields. It is method of inference using Bayes' rule to update the estimation of probability for the certain hypothesis when additional evidences are acquired. According to the current researches, malfunction of nuclear power plant can be detected by using this Bayesian inference which consistently piles up the newly incoming data and updates its estimation. However, those researches are based on the assumption that people are doing like computer perfectly, which can be criticized and may cause a problem in real world application. Studies in cognitive psychology indicates that when the amount of information becomes larger, people can't save the whole data because people have limited memory capacity which is well known as working memory, and also they have attention problem. The purpose of this paper is to consider the psychological factors and confirm how much this working memory and attention will affect the resulted estimation based on the Bayesian inference. To confirm this, experiment on human is needed, and the tool of experiment is Compact Nuclear Simulator (CNS)
Ayele, Yonas Zewdu
2016-01-01
The papers of this thesis are not available in Munin. Paper I. Ayele YZ, Barabadi A, Barabady J.: A methodology for identification of a suitable drilling waste handling system in the Arctic region. (Manuscript). Paper II. Ayele YZ, Barabady J, Droguett EL.: Dynamic Bayesian network based risk assessment for Arctic offshore drilling waste handling practices. (Manuscript). Published version available in Journal of Offshore Mechanics and Arctic Engineering 138(5), 051302 (Jun 17, 2016) ...
Rubin, Donald B.
1981-01-01
The Bayesian bootstrap is the Bayesian analogue of the bootstrap. Instead of simulating the sampling distribution of a statistic estimating a parameter, the Bayesian bootstrap simulates the posterior distribution of the parameter; operationally and inferentially the methods are quite similar. Because both methods of drawing inferences are based on somewhat peculiar model assumptions and the resulting inferences are generally sensitive to these assumptions, neither method should be applied wit...
GLASS 2.0: An Operational, Multimodal, Bayesian Earthquake Data Association Engine
Benz, H.; Johnson, C. E.; Patton, J. M.; McMahon, N. D.; Earle, P. S.
2015-12-01
The legacy approach to automated detection and determination of hypocenters is arrival time stacking algorithms. Examples of such algorithms are the associator, Binder, which has been in continuous use in many USGS-supported regional seismic networks since the 1980s and the spherical earth successor, GLASS 1.0, currently in service at the USGS National Earthquake Information Center for over 10 years. The principle short-comings of the legacy approach are 1) it can only use phase arrival times, 2) it does not adequately address the problems of extreme variations in station density worldwide, 3) it cannot incorporate multiple phase models or statistical attributes of phases with distance, and 4) it cannot incorporate noise model attributes of individual stations. Previously we introduced a theoretical framework of a new associator using a Bayesian kernel stacking approach to approximate a joint probability density function for hypocenter localization. More recently we added station- and phase-specific Bayesian constraints to the association process. GLASS 2.0 incorporates a multiplicity of earthquake related data including phase arrival times, back-azimuth and slowness information from array beamforming, arrival times from waveform cross correlation processing, and geographic constraints from real-time social media reports of ground shaking. We demonstrate its application by modeling an aftershock sequence using dozens of stations that recorded tens of thousands of earthquakes over a period of one month. We also demonstrate Glass 2.0 performance regionally and teleseismically using the globally distributed real-time monitoring system at NEIC.
Di Troia, Claudio
2015-01-01
A class of parametric distribution functions has been proposed in [C.DiTroia, Plasma Physics and Controlled Fusion,54,2012] as equilibrium distribution functions (EDFs) for charged particles in fusion plasmas, representing supra-thermal particles in anisotropic equilibria for Neutral Beam Injection, Ion Cyclotron Heating scenarios. Moreover, the EDFs can also represent nearly isotropic equilibria for Slowing-Down $alpha$ particles and core thermal plasma populations. These EDFs depend on constants of motion (COMs). Assuming an axisymmetric system with no equilibrium electric field, the EDF depends on the toroidal canonical momentum $P_\\phi$, the kinetic energy $w$ and the magnetic moment \\mu. In the present work, the EDFs are obtained from first principles and general hypothesis. The derivation is probabilistic and makes use of the Bayes' Theorem. The bayesian argument allows us to describe how far from the prior probability distribution function (pdf), e.g. Maxwellian, the plasma is, based on the information...
Energy Technology Data Exchange (ETDEWEB)
DALE, R.N.
2000-05-01
A system to accommodate the removal of long-length contaminated equipment (LLCE) from Hanford underground radioactive waste storage tanks was designed, procured, and demonstrated, via a project activity during the 1990s. The system is the Long Length Contaminated Equipment Removal System (LLCERS). LLCERS will be maintained and operated by Tank Farms Engineering and Operations organizations and other varied projects having a need for the system. The responsibility for the operation and maintenance of the LLCERS Receiver Trailer (RT) and Transport Trailer (TT) resides with the RPP Characterization Project Operations organization. The purpose of this document is to provide vendor supplied operating and maintenance (O & M) information for the RT and TT in a readily retrievable form. This information is provided this way instead of in a vendor information (VI) file to maintain configuration control of the operations baseline as described in RPP-6085, ''Configuration Management Plan for Long Length Contaminated Equipment Receiver and Transport Trailers''. Additional Operations Baseline documents are identified in RPP-6085.
International Nuclear Information System (INIS)
A system to accommodate the removal of long-length contaminated equipment (LLCE) from Hanford underground radioactive waste storage tanks was designed, procured, and demonstrated, via a project activity during the 1990s. The system is the Long Length Contaminated Equipment Removal System (LLCERS). LLCERS will be maintained and operated by Tank Farms Engineering and Operations organizations and other varied projects having a need for the system. The responsibility for the operation and maintenance of the LLCERS Receiver Trailer (RT) and Transport Trailer (TT) resides with the RPP Characterization Project Operations organization. The purpose of this document is to provide vendor supplied operating and maintenance (O and M) information for the RT and TT in a readily retrievable form. This information is provided this way instead of in a vendor information (VI) file to maintain configuration control of the operations baseline as described in RPP-6085, ''Configuration Management Plan for Long Length Contaminated Equipment Receiver and Transport Trailers''. Additional Operations Baseline documents are identified in RPP-6085
Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables
Morrison, Ann Michelle; Coughlin, Kelly; Shine, James P.; Coull, Brent A.; Rex, Andrea C.
2003-01-01
Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability...
Energy Technology Data Exchange (ETDEWEB)
Neises, T. W.; Wagner, M. J.; Gray, A. K.
2014-04-01
Research of advanced power cycles has shown supercritical carbon dioxide power cycles may have thermal efficiency benefits relative to steam cycles at temperatures around 500 - 700 degrees C. To realize these benefits for CSP, it is necessary to increase the maximum outlet temperature of current tower designs. Research at NREL is investigating a concept that uses high-pressure supercritical carbon dioxide as the heat transfer fluid to achieve a 650 degrees C receiver outlet temperature. At these operating conditions, creep becomes an important factor in the design of a tubular receiver and contemporary design assumptions for both solar and traditional boiler applications must be revisited and revised. This paper discusses lessons learned for high-pressure, high-temperature tubular receiver design. An analysis of a simplified receiver tube is discussed, and the results show the limiting stress mechanisms in the tube and the impact on the maximum allowable flux as design parameters vary. Results of this preliminary analysis indicate an underlying trade-off between tube thickness and the maximum allowable flux on the tube. Future work will expand the scope of design variables considered and attempt to optimize the design based on cost and performance metrics.
Dual channel 115 and 230 GHz SIS receivers in operation at the Owens Valley Radio Observatory
International Nuclear Information System (INIS)
The Owens Valley Radio Observatory millimeter-wave interferometer array is presently operating with dual channel SIS tunnel junction receivers. The first channel covers the frequency range from 85 to 120 GHz and the second channel covers the frequency range from 200 to 300 GHz. The mixers consist of a corrugated feedhorn, single-stage circular to rectangular waveguide transition, reduced-height waveguide with an SIS junction mounted across the E-plane and a non-contacting backshort. The mixer block has a built-in RF choke for the IF signal path which is designed to present a short circuit to the junction at frequencies above the 2 GHz IF frequency. The small area (<1 μm/sup 2/) PbInAu-native oxide-PbAu SIS tunnel junctions are fabricated using a bridge lift-off technique. The LO power is provided by Gunn oscillators followed by doublers or triplers. The receivers in the 85 to 120 GHz band have noise temperatures of <100 K, while the receivers in the 200 to 300 GHz band have noise temperatures in the range from 200 to 300 K. These dual channel receivers are mounted in 4.5 K closed cycle refrigerators. They are in continuous use on the three element millimeter-wavelength interferometer array
Kim, Minsoo; Kim, Yejin; Kim, Hyosoo; Piao, Wenhua; Kim, Changwon
2016-06-01
An operator decision support system (ODSS) is proposed to support operators of wastewater treatment plants (WWTPs) in making appropriate decisions. This system accounts for water quality (WQ) variations in WWTP influent and effluent and in the receiving water body (RWB). The proposed system is comprised of two diagnosis modules, three prediction modules, and a scenario-based supporting module (SSM). In the diagnosis modules, the WQs of the influent and effluent WWTP and of the RWB are assessed via multivariate analysis. Three prediction modules based on the k-nearest neighbors (k-NN) method, activated sludge model no. 2d (ASM2d) model, and QUAL2E model are used to forecast WQs for 3 days in advance. To compare various operating alternatives, SSM is applied to test various predetermined operating conditions in terms of overall oxygen transfer coefficient (Kla), waste sludge flow rate (Qw), return sludge flow rate (Qr), and internal recycle flow rate (Qir). In the case of unacceptable total phosphorus (TP), SSM provides appropriate information for the chemical treatment. The constructed ODSS was tested using data collected from Geumho River, which was the RWB, and S WWTP in Daegu City, South Korea. The results demonstrate the capability of the proposed ODSS to provide WWTP operators with more objective qualitative and quantitative assessments of WWTP and RWB WQs. Moreover, the current study shows that ODSS, using data collected from the study area, can be used to identify operational alternatives through SSM at an integrated urban wastewater management level. PMID:26893178
Bredemeier, Keith; Spielberg, Jeffrey M; Silton, Rebecca Levin; Berenbaum, Howard; Heller, Wendy; Miller, Gregory A.
2010-01-01
The present study examined the utility of the anhedonic depression scale from the Mood and Anxiety Symptoms Questionnaire (MASQ-AD) as a way to screen for depressive disorders. Using receiver-operator characteristic analysis, the sensitivity and specificity of the full 22-item MASQ-AD scale, as well as the 8 and 14-item subscales, were examined in relation to both current and lifetime DSM-IV depressive disorder diagnoses in two nonpatient samples. As a means of comparison, the sensitivity and...
Bayesian Methods for Medical Test Accuracy
Directory of Open Access Journals (Sweden)
Lyle D. Broemeling
2011-05-01
Full Text Available Bayesian methods for medical test accuracy are presented, beginning with the basic measures for tests with binary scores: true positive fraction, false positive fraction, positive predictive values, and negative predictive value. The Bayesian approach is taken because of its efficient use of prior information, and the analysis is executed with a Bayesian software package WinBUGS®. The ROC (receiver operating characteristic curve gives the intrinsic accuracy of medical tests that have ordinal or continuous scores, and the Bayesian approach is illustrated with many examples from cancer and other diseases. Medical tests include X-ray, mammography, ultrasound, computed tomography, magnetic resonance imaging, nuclear medicine and tests based on biomarkers, such as blood glucose values for diabetes. The presentation continues with more specialized methods suitable for measuring the accuracies of clinical studies that have verification bias, and medical tests without a gold standard. Lastly, the review is concluded with Bayesian methods for measuring the accuracy of the combination of two or more tests.
International Nuclear Information System (INIS)
Objective: To evaluate the Lintula score in reducing negative appendectomies in the adult population. Study Design: Descriptive analytical study. Place and Duration of Study: Surgical Department, Khyber Teaching Hospital, Peshawar, Pakistan, from August 2012 to April 2014. Methodology: A total of 408 emergency patients with a clinical diagnosis of acute appendicitis were included in the study. True or negative appendectomy status was determined per-operatively. Lintula score was calculated afterwards and evaluated for various cut-off points. Results: Among the study population, 72 (17.6%) had a normal appendix by operative assessment and 336 (82.4%) had an acutely inflammed appendix. The receiver operating characteristic curve showed that the optimal cut-off point was /Sup 2/ 21 with 100% sensitivity, 88.4% specificity and positive and negative predictive values of 97.3% and 100%, respectively. Area under the curve was 0.963 with 90.4% overall accuracy. Conclusion: Utilizing the Lintula cut-off point of /Sup 2/ 21, negative appendectomies, unnecessary admissions and healthcare cost can all be reduced. (author)
International Nuclear Information System (INIS)
The DOE Office of Storage and Transportation Systems is responsible for the development and management of a transportation system to provide all the necessary services for the transportation of the spent fuel and wastes from reactor sites to repositories. DOE/ORO has requested Oak Ridge Associated Universities (ORAU) to assist DOE in developing rosters of sources of transportation expertise in: (1) carrier operations; (2) transportation management, planning, and logistics; (3) transportation equipment; (4) transportation facilities design and operation; (5) vehicle safety; and (6) transportation operations quality assurance; as related to truck, rail, barge, and intermodal transportation. Persons or organizations with experience in shipping of non-hazardous materials, spent nuclear fuel, other radioactive materials, and/or other hazardous materials were included in the information system. A mailed inquiry was sent to over 2300 potential sources of transportation expertise. Responses were received from 207 persons and 254 organizations. Section 1 contains the identification numbers of the individuals and organizations that responded. Section 2 contains identification codes, names, addresses, and phone numbers of each of the individual and organization respondents. The reader can refer to Section 2 for the name and address of the respondents for the identification codes listed for each technical area/experience base in Section 1
Energy Technology Data Exchange (ETDEWEB)
1987-01-01
The DOE Office of Storage and Transportation Systems is responsible for the development and management of a transportation system to provide all the necessary services for the transportation of the spent fuel and wastes from reactor sites to repositories. DOE/ORO has requested Oak Ridge Associated Universities (ORAU) to assist DOE in developing rosters of sources of transportation expertise in: (1) carrier operations; (2) transportation management, planning, and logistics; (3) transportation equipment; (4) transportation facilities design and operation; (5) vehicle safety; and (6) transportation operations quality assurance; as related to truck, rail, barge, and intermodal transportation. Persons or organizations with experience in shipping of non-hazardous materials, spent nuclear fuel, other radioactive materials, and/or other hazardous materials were included in the information system. A mailed inquiry was sent to over 2300 potential sources of transportation expertise. Responses were received from 207 persons and 254 organizations. Section 1 contains the identification numbers of the individuals and organizations that responded. Section 2 contains identification codes, names, addresses, and phone numbers of each of the individual and organization respondents. The reader can refer to Section 2 for the name and address of the respondents for the identification codes listed for each technical area/experience base in Section 1.
DEFF Research Database (Denmark)
Liu, Dedi; Li, Xiang; Guo, Shenglian;
2015-01-01
as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the...... Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir....... inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental...
International Nuclear Information System (INIS)
ROC examination was based on patient computer tomograms, in which lesions of the liver were simulated by image manipulation. The window width for optimal recognition of the lesions was found to be 128 Hounsfield units (HU). Evaluation of Receiver Operating Characteristics (ROC) by diameters and contrasts of the lesions yielded a contrast detail diagram largely reflecting conditions in clinical practice. The detectability - measured via the smallest visible diameter of the lesions - is inferior by the factor 3 to 60 to the results obtained with hole pattern phantoms. For example, in computer tomograms lesions of the liver of 24 mm and 4 HU or also 11 mm and 10 HU yield 70% true positive and 10% false positive findings. (orig.)
ROCS: receiver operating characteristic surface for class-skewed high-throughput data.
Directory of Open Access Journals (Sweden)
Tianwei Yu
Full Text Available The receiver operating characteristic (ROC curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the curve (AUC insufficient for the purpose of judging classifier performance. Here we define an ROC surface (ROCS using true positive rate (TPR, false positive rate (FPR, and true discovery rate (TDR. The ROC surface, together with the associated quantities, volume under the surface (VUS and FDR-controlled area under the ROC curve (FCAUC, provide a useful approach for gauging classifier performance on class-skewed high-throughput data. The implementation as an R package is available at http://userwww.service.emory.edu/~tyu8/ROCS/.
25 CFR 47.3 - How does a Bureau-operated school find out how much funding it will receive?
2010-04-01
... 25 Indians 1 2010-04-01 2010-04-01 false How does a Bureau-operated school find out how much... EDUCATION UNIFORM DIRECT FUNDING AND SUPPORT FOR BUREAU-OPERATED SCHOOLS § 47.3 How does a Bureau-operated school find out how much funding it will receive? The Office of Indian Education Programs (OIEP)...
Slotnick, Scott D; Jeye, Brittany M; Dodson, Chad S
2016-01-01
Is recollection a continuous/graded process or a threshold/all-or-none process? Receiver operating characteristic (ROC) analysis can answer this question as the continuous model and the threshold model predict curved and linear recollection ROCs, respectively. As memory for plurality, an item's previous singular or plural form, is assumed to rely on recollection, the nature of recollection can be investigated by evaluating plurality memory ROCs. The present study consisted of four experiments. During encoding, words (singular or plural) or objects (single/singular or duplicate/plural) were presented. During retrieval, old items with the same plurality or different plurality were presented. For each item, participants made a confidence rating ranging from "very sure old", which was correct for same plurality items, to "very sure new", which was correct for different plurality items. Each plurality memory ROC was the proportion of same versus different plurality items classified as "old" (i.e., hits versus false alarms). Chi-squared analysis revealed that all of the plurality memory ROCs were adequately fit by the continuous unequal variance model, whereas none of the ROCs were adequately fit by the two-high threshold model. These plurality memory ROC results indicate recollection is a continuous process, which complements previous source memory and associative memory ROC findings. PMID:25354207
Energy Technology Data Exchange (ETDEWEB)
Takahashi, Yasuyuki; Murase, Kenya [Osaka Medical Coll., Takatsuki (Japan). Graduate School; Higashino, Hiroshi; Sogabe, Ichiro; Sakamoto, Kana
2001-12-01
The quality of images reconstructed by means of the maximum likelihood-expectation maximization (ML-EM) and ordered subset (OS)-EM algorithms, was examined with parameters such as the number of iterations and subsets, then compared with the quality of images reconstructed by the filtered back projection method. Phantoms showing signals inside signals, which mimicked single-photon emission computed tomography (SPECT) images of cerebral blood flow and myocardial perfusion, and phantoms showing signals around the signals obtained by SPECT of bone and tumor were used for experiments. To determine signals for recognition, SPECT images in which the signals could be appropriately recognized with a combination of fewer iterations and subsets of different sizes and densities were evaluated by receiver operating characteristic (ROC) analysis. The results of ROC analysis were applied to myocardial phantom experiments and scintigraphy of myocardial perfusion. Taking the image processing time into consideration, good SPECT images were obtained by OS-EM at iteration No. 10 and subset 5. This study will be helpful for selection of parameters such as the number of iterations and subsets when using the ML-EM or OS-EM algorithms. (author)
Wunderlich, Adam; Goossens, Bart
2014-10-01
In an effort to generalize task-based assessment beyond traditional signal detection, there is a growing interest in performance evaluation for combined detection and estimation tasks, in which signal parameters, such as size, orientation, and contrast are unknown and must be estimated. One motivation for studying such tasks is their rich complexity, which offers potential advantages for imaging system optimization. To evaluate observer performance on combined detection and estimation tasks, Clarkson introduced the estimation receiver operating characteristic (EROC) curve and the area under the EROC curve as a summary figure of merit. This work provides practical tools for EROC analysis of experimental data. In particular, we propose nonparametric estimators for the EROC curve, the area under the EROC curve, and for the variance/covariance matrix of a vector of correlated EROC area estimates. In addition, we show that reliable confidence intervals can be obtained for EROC area, and we validate these intervals with Monte Carlo simulation. Application of our methodology is illustrated with an example comparing magnetic resonance imaging [Formula: see text]-space sampling trajectories. MATLAB® software implementing the EROC analysis estimators described in this work is publicly available at http://code.google.com/p/iqmodelo/. PMID:26158044
Dust emissions from grain elevator operations can be a safety and health risk, and a nuisance. Dust emission and air entrainment data are needed for designing adequate and effective control methods. This study measured the dust emitted and air entrained during corn receiving operations at an eleva...
Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses
Sarlis, Nicholas V.; Christopoulos, Stavros-Richard G.
2014-03-01
The Receiver Operating Characteristics (ROC) is used for the evaluation of prediction methods in various disciplines like meteorology, geophysics, complex system physics, medicine etc. The estimation of the significance of a binary prediction method, however, remains a cumbersome task and is usually done by repeating the calculations by Monte Carlo. The FORTRAN code provided here simplifies this problem by evaluating the significance of binary predictions for a family of ellipses which are based on confidence ellipses and cover the whole ROC space. Catalogue identifier: AERY_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERY_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 11511 No. of bytes in distributed program, including test data, etc.: 72906 Distribution format: tar.gz Programming language: FORTRAN. Computer: Any computer supporting a GNU FORTRAN compiler. Operating system: Linux, MacOS, Windows. RAM: 1Mbyte Classification: 4.13, 9, 14. Nature of problem: The Receiver Operating Characteristics (ROC) is used for the evaluation of prediction methods in various disciplines like meteorology, geophysics, complex system physics, medicine etc. The estimation of the significance of a binary prediction method, however, remains a cumbersome task and is usually done by repeating the calculations by Monte Carlo. The FORTRAN code provided here simplifies this problem by evaluating the significance of binary predictions for a family of ellipses which are based on confidence ellipses and cover the whole ROC space. Solution method: Using the statistics of random binary predictions for a given value of the predictor threshold ɛt, one can construct the corresponding confidence ellipses. The envelope of these corresponding confidence ellipses is estimated when
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
Draper, D.
2001-01-01
© 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
Full receiver operating characteristic curve estimation using two alternative forced choice studies.
Massanes, Francesc; Brankov, Jovan G
2016-01-01
Task-based medical image quality is typically measured by the degree to which a human observer can perform a diagnostic task in a psychophysical human observer study. During a typical study, an observer is asked to provide a numerical score quantifying his confidence as to whether an image contains a diagnostic marker or not. Such scores are then used to measure the observers' diagnostic accuracy, summarized by the receiver operating characteristic (ROC) curve and the area under ROC curve. These types of human studies are difficult to arrange, costly, and time consuming. In addition, human observers involved in this type of study should be experts on the image genre to avoid inconsistent scoring through the lengthy study. In two-alternative forced choice (2AFC) studies, known to be faster, two images are compared simultaneously and a single indicator is given. Unfortunately, the 2AFC approach cannot lead to a full ROC curve or a set of image scores. The aim of this work is to propose a methodology in which multiple rounds of the 2AFC studies are used to re-estimate an image confidence score (a.k.a. rating, ranking) and generate the full ROC curve. In the proposed approach, we treat image confidence score as an unknown rating that needs to be estimated and 2AFC as a two-player match game. To achieve this, we use the ELO rating system, which is used for calculating the relative skill levels of players in competitor-versus-competitor games such as chess. The proposed methodology is not limited to ELO, and other rating methods such as TrueSkill™, Chessmetrics, or Glicko can be also used. The presented results, using simulated data, indicate that a full ROC curve can be recovered using several rounds of 2AFC studies and that the best pairing strategy starts with the first round of pairing abnormal versus normal images (as in the classical 2AFC approach) followed by a number of rounds using random pairing. In addition, the proposed method was tested in a pilot human
DEFF Research Database (Denmark)
Jensen, Kasper Lynge; Toftum, Jørn; Friis-Hansen, Peter
2009-01-01
building design. In this paper, focus will be on the effects of temperature on mental performance and not on other indoor climate factors. A total economic comparison of six different building designs, four located in northern Europe and two in Los Angeles, USA, was performed. The results indicate...... that investments in improved indoor thermal conditions can be justified economically in most cases. The Bayesian Network provides a reliable platform using probabilities for modelling the complexity while estimating the effect of indoor climate factors on human beings, due to the different ways in which humans...
Bredemeier, Keith; Spielberg, Jeffery M.; Silton, Rebecca Levin; Berenbaum, Howard; Heller, Wendy; Miller, Gregory A.
2010-01-01
The present study examined the utility of the anhedonic depression scale from the Mood and Anxiety Symptoms Questionnaire (MASQ-AD scale) as a way to screen for depressive disorders. Using receiver-operating characteristic analysis, we examined the sensitivity and specificity of the full 22-item MASQ-AD scale, as well as the 8- and 14-item…
Blossom blight forecasting is an important aspect of fire blight, caused by Erwinia amylovora, management for both apple and pear. A comparison of the forecast accuracy of two common fire blight forecasters, MARYBLYT and Cougarblight, was performed with receiver operating characteristic (ROC) curve ...
Riggio, V.; Pesce, L.L.; Morreale, S.; Portolano, B.
2013-01-01
Using receiver-operating characteristic (ROC) curve methodology this study was designed to assess the diagnostic effectiveness of somatic cell count (SCC) and the California mastitis test (CMT) in Valle del Belice sheep, and to propose and evaluate threshold values for those tests that would optimal
Yovanoff, P.; Squires, J.
2006-01-01
Two different theoretical approaches were compared to determine the optimal cutoff scores for the Ages and Stages Questionnaires: Social-Emotional (ASQ: SE), a social-emotional screening test. Cutoff scores based on statistical decision theory modeling, Receiver Operator Characteristics (ROC), were compared with cutoff scores obtained using Item…
International Nuclear Information System (INIS)
The small-scale open and direct solar thermal Brayton cycle with recuperator has several advantages, including low cost, low operation and maintenance costs and it is highly recommended. The main disadvantages of this cycle are the pressure losses in the recuperator and receiver, turbomachine efficiencies and recuperator effectiveness, which limit the net power output of such a system. The irreversibilities of the solar thermal Brayton cycle are mainly due to heat transfer across a finite temperature difference and fluid friction. In this paper, thermodynamic optimisation is applied to concentrate on these disadvantages in order to optimise the receiver and recuperator and to maximise the net power output of the system at various steady-state conditions, limited to various constraints. The effects of wind, receiver inclination, rim angle, atmospheric temperature and pressure, recuperator height, solar irradiance and concentration ratio on the optimum geometries and performance were investigated. The dynamic trajectory optimisation method was applied. Operating points of a standard micro-turbine operating at its highest compressor efficiency and a parabolic dish concentrator diameter of 16 m were considered. The optimum geometries, minimum irreversibility rates and maximum receiver surface temperatures of the optimised systems are shown. For an environment with specific conditions and constraints, there exists an optimum receiver and recuperator geometry so that the system produces maximum net power output. -- Highlights: → Optimum geometries exist such that the system produces maximum net power output. → Optimum operating conditions are shown. → Minimum irreversibility rates and minimum entropy generation rates are shown. → Net power output was described in terms of total entropy generation rate. → Effects such as wind, recuperator height and irradiance were investigated.
Directory of Open Access Journals (Sweden)
Gabriella Ferruzzi
2013-02-01
Full Text Available A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.
Kirstein, Roland
2005-01-01
This paper presents a modification of the inspection game: The ?Bayesian Monitoring? model rests on the assumption that judges are interested in enforcing compliant behavior and making correct decisions. They may base their judgements on an informative but imperfect signal which can be generated costlessly. In the original inspection game, monitoring is costly and generates a perfectly informative signal. While the inspection game has only one mixed strategy equilibrium, three Perfect Bayesia...
International Nuclear Information System (INIS)
The proposed action is to issue a materials license, pursuant to 10 CFR Parts 30, 40 and 70 of the Commission's regulations, authorizing Allied-General Nuclear Services to receive and handle fuel casks containing spent reactor fuel elements and to store spent reactor fuel at the Barnwell Nuclear Fuel Plant (BNFP), in the Barnwell Fuel Receiving and Storage Station (BFRSS). The BFRSS is a part of, and contiguous to, the BNFP-Separations Facility which is being constructed on a small portion of a 1700 acre site about six miles west of the city of Barnwell in Barnwell County, South Carolina. Construction of the BFRSS facility has been completed and the BNFP Separations Facility is more than 90% complete. A uranium Hexafluoride Facility is being constructed on the same site, and a Plutonium Product Facility is proposed to be constructed adjacent to the Separations Facility. The license that is the subject of this action will, if issued, allow lthe use of the BFRSS separate4 from the operation of the Separations Facility. Impacts resulting from the construction of the BFRSS have already occurred and mitigating measures have been and are being implemented to offset any adverse impacts. Operation of the BFRSS will not interfere with water sources, and should cause no noticeable damage to the terrestrial or aquatic environments. Operating experience at other fuel receiving and storage facilities has shown that radioactive concentrations discharged to the environs (the more significant process effluents) have been well below applicabhle state and federal limits. The small quantities to be released during operation of the BFRSS will result in negligible environmental impact. 20 figs
Approximate Bayesian recursive estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav
2014-01-01
Roč. 285, č. 1 (2014), s. 100-111. ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Approximate parameter estimation * Bayesian recursive estimation * Kullback–Leibler divergence * Forgetting Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.038, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/karny-0425539.pdf
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
Generazio, Edward R.
2014-01-01
Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.
Energy Technology Data Exchange (ETDEWEB)
Harris, S.; Gross, R.; Mitchell, E.
2011-01-18
The Savannah River Site (SRS) spring operated pressure relief valve (SORV) maintenance intervals were evaluated using an approach provided by the American Petroleum Institute (API RP 581) for risk-based inspection technology (RBI). In addition, the impact of extending the inspection schedule was evaluated using Monte Carlo Simulation (MCS). The API RP 581 approach is characterized as a Weibull analysis with modified Bayesian updating provided by SRS SORV proof testing experience. Initial Weibull parameter estimates were updated as per SRS's historical proof test records contained in the Center for Chemical Process Safety (CCPS) Process Equipment Reliability Database (PERD). The API RP 581 methodology was used to estimate the SORV's probability of failing on demand (PFD), and the annual expected risk. The API RP 581 methodology indicates that the current SRS maintenance plan is conservative. Cost savings may be attained in certain mild service applications that present low PFD and overall risk. Current practices are reviewed and recommendations are made for extending inspection intervals. The paper gives an illustration of the inspection costs versus the associated risks by using API RP 581 Risk Based Inspection (RBI) Technology. A cost effective maintenance frequency balancing both financial risk and inspection cost is demonstrated.
Directory of Open Access Journals (Sweden)
Hiroshi Saito
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.
International Nuclear Information System (INIS)
Five radiographic protocols for detecting pulmonary metastases in dogs were compared by analyzing receiver operating characteristic curves for the protocols. Protocols compared were a right lateral view only, a left lateral view only, right lateral and dorsoventral views, both lateral views, and all 3 views. Three radiologists used each of the protocols to evaluate 99 sets of thoracic radiographs. Fifty-two sets of radiographs were from dogs confirmed histologically to have pulmonary metastases and 47 were from dogs proven at necropsy to be free of pulmonary metastases. Results of the 5 protocols were not statistically different. We concluded that a third view is not necessary when routinely screening dogs with cancer for pulmonary metastases and that the standard 2-view thoracic examination should be adequate. However, in individual cases, a third view may be the determining factor in establishing a radiographic diagnosis and should be obtained if any suspicious areas are seen
Thurner, S; Lowen, S B; Teich, M C; Thurner, Stefan; Feurstein, Markus C.; Lowen, Steven B.; Teich, Malvin C.
1998-01-01
Receiver-operating-characteristic (ROC) analysis was used to assess the suitability of various heart rate variability (HRV) measures for correctly classifying electrocardiogram records of varying lengths as normal or revealing the presence of heart failure. Scale-dependent HRV measures were found to be substantially superior to scale-independent measures (scaling exponents) for discriminating the two classes of data over a broad range of record lengths. The wavelet-coefficient standard deviation at a scale near 32 heartbeat intervals, and its spectral counterpart near 1/32 cycles per interval, provide reliable results using record lengths just minutes long. A jittered integrate-and-fire model built around a fractal Gaussian-noise kernel provides a realistic, though not perfect, simulation of heartbeat sequences.
Ghazvinei, P. T.; Zandi, J.; Ariffin, J.; Hashim, R. B.; Motamedi, S.; Aghamohammadi, N.; Moghaddam, D. A.
2015-10-01
Soil erosion is undesirable natural event that causes land degradation and desertification. Identify the erosion-prone areas is a major component of preventive measures. Recent landslide damages at different regions lead us to develop a model of the erosion susceptibility map using empirical method (RUSLE). A landslide-location map was established by interpreting satellite image. Field observation data was used to validate the intensity of soil erosion. Further, a correlation analysis was conducted to investigate the "Receiver Operating Characteristic" and frequency ratio. Results showed a satisfactory correlation between the prepared RUSLE-based soil erosion map and actual landslide distribution. The proposed model can effectively predict the landslide events in soil-erosion area. Such a reliable predictive model is an effective management facility for the regional landslide forecasting system.
Directory of Open Access Journals (Sweden)
P. T. Ghazvinei
2015-10-01
Full Text Available Soil erosion is undesirable natural event that causes land degradation and desertification. Identify the erosion-prone areas is a major component of preventive measures. Recent landslide damages at different regions lead us to develop a model of the erosion susceptibility map using empirical method (RUSLE. A landslide-location map was established by interpreting satellite image. Field observation data was used to validate the intensity of soil erosion. Further, a correlation analysis was conducted to investigate the "Receiver Operating Characteristic" and frequency ratio. Results showed a satisfactory correlation between the prepared RUSLE-based soil erosion map and actual landslide distribution. The proposed model can effectively predict the landslide events in soil-erosion area. Such a reliable predictive model is an effective management facility for the regional landslide forecasting system.
Directory of Open Access Journals (Sweden)
Pešić Milan P.
2012-01-01
Full Text Available A numerical simulation of the radiological consequences of the RB reactor reactivity excursion accident, which occurred on October 15, 1958, and an estimation of the total doses received by the operators were run by the MCNP5 computer code. The simulation was carried out under the same assumptions as those used in the 1960 IAEA-organized experimental simulation of the accident: total fission energy of 80 MJ released in the accident and the frozen positions of the operators. The time interval of exposure to high doses received by the operators has been estimated. Data on the RB1/1958 reactor core relevant to the accident are given. A short summary of the accident scenario has been updated. A 3-D model of the reactor room and the RB reactor tank, with all the details of the core, created. For dose determination, 3-D simplified, homogenised, sexless and faceless phantoms, placed inside the reactor room, have been developed. The code was run for a number of neutron histories which have given a dose rate uncertainty of less than 2%. For the determination of radiation spectra escaping the reactor core and radiation interaction in the tissue of the phantoms, the MCNP5 code was run (in the KCODE option and “mode n p e”, with a 55-group neutron spectra, 35-group gamma ray spectra and a 10-group electron spectra. The doses were determined by using the conversion of flux density (obtained by the F4 tally in the phantoms to doses using factors taken from ICRP-74 and from the deposited energy of neutrons and gamma rays (obtained by the F6 tally in the phantoms’ tissue. A rough estimation of the time moment when the odour of ozone was sensed by the operators is estimated for the first time and given in Appendix A.1. Calculated total absorbed and equivalent doses are compared to the previously reported ones and an attempt to understand and explain the reasons for the obtained differences has been made. A Root Cause Analysis of the accident was done and
Decentralized Distributed Bayesian Estimation
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
Praha: ÚTIA AVČR, v.v.i, 2011 - (Janžura, M.; Ivánek, J.). s. 16-16 [7th International Workshop on Data–Algorithms–Decision Making. 27.11.2011-29.11.2011, Mariánská] R&D Projects: GA ČR 102/08/0567; GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : estimation * distributed estimation * model Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/AS/dedecius-decentralized distributed bayesian estimation.pdf
Bayesian Agglomerative Clustering with Coalescents
Teh, Yee Whye; Daumé III, Hal; Roy, Daniel
2009-01-01
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate our approach in document clustering and phylolinguistics.
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
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 and...... largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network...
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that system...
International Nuclear Information System (INIS)
This paper will report on the progress to date in carrying out Receiver Operating Characteristics (ROC) studies comparing Maximum Likelihood Estimator (MLE) and Filtered Backprojection (FBP) reconstructions of normal and abnormal human brain PET data in a clinical setting. A previous statistical study of reconstructions of the Hoffman brain phantom with real data indicated that the pixel-to-pixel standard deviation in feasible MLE images is approximately proportional to the square root of the number of counts in a region, as opposed to a standard deviation which is high and largely independent of the number of counts in FBP. A preliminary ROC study carried out with 10 non-medical observers performing a relatively simple detectability task indicates that, for the majority of observers, lower standard deviation translates itself into a statistically significant detectability advantage in MLE reconstructions. The initial results of ongoing tests with four experienced neurologists/nuclear medicine physicians are presented. Normal cases of 18F -- fluorodeoxyglucose (FDG) cerebral metabolism studies and abnormal cases in which a variety of lesions have been introduced into normal data sets have been evaluated. We report on the results of reading the reconstructions of 90 data sets, each corresponding to a single brain slice. It has become apparent that the design of the study based on reading single brain slices is too insensitive and we propose a variation based on reading three consecutive slices at a time, rating only the center slice. 9 refs., 2 figs., 1 tab
Energy Technology Data Exchange (ETDEWEB)
Llacer, J.; Veklerov, E.; Nolan, D. (Lawrence Berkeley Lab., CA (USA)); Grafton, S.T.; Mazziotta, J.C.; Hawkins, R.A.; Hoh, C.K.; Hoffman, E.J. (California Univ., Los Angeles, CA (USA))
1990-10-01
This paper will report on the progress to date in carrying out Receiver Operating Characteristics (ROC) studies comparing Maximum Likelihood Estimator (MLE) and Filtered Backprojection (FBP) reconstructions of normal and abnormal human brain PET data in a clinical setting. A previous statistical study of reconstructions of the Hoffman brain phantom with real data indicated that the pixel-to-pixel standard deviation in feasible MLE images is approximately proportional to the square root of the number of counts in a region, as opposed to a standard deviation which is high and largely independent of the number of counts in FBP. A preliminary ROC study carried out with 10 non-medical observers performing a relatively simple detectability task indicates that, for the majority of observers, lower standard deviation translates itself into a statistically significant detectability advantage in MLE reconstructions. The initial results of ongoing tests with four experienced neurologists/nuclear medicine physicians are presented. Normal cases of {sup 18}F -- fluorodeoxyglucose (FDG) cerebral metabolism studies and abnormal cases in which a variety of lesions have been introduced into normal data sets have been evaluated. We report on the results of reading the reconstructions of 90 data sets, each corresponding to a single brain slice. It has become apparent that the design of the study based on reading single brain slices is too insensitive and we propose a variation based on reading three consecutive slices at a time, rating only the center slice. 9 refs., 2 figs., 1 tab.
Riggio, Valentina; Pesce, Lorenzo L; Morreale, Salvatore; Portolano, Baldassare
2013-06-01
Using receiver-operating characteristic (ROC) curve methodology this study was designed to assess the diagnostic effectiveness of somatic cell count (SCC) and the California mastitis test (CMT) in Valle del Belice sheep, and to propose and evaluate threshold values for those tests that would optimally discriminate between healthy and infected udders. Milk samples (n=1357) were collected from 684 sheep in four flocks. The prevalence of infection, as determined by positive bacterial culture was 0.36, 87.7% of which were minor and 12.3% major pathogens. Of the culture negative samples, 83.7% had an SCCCMT results were evaluated, the estimated area under the ROC curve was greater for glands infected with major compared to minor pathogens (0.88 vs. 0.73), whereas the area under the curve considering all pathogens was similar to the one for minor pathogens (0.75). The estimated optimal thresholds were 3.00 (CMT), 2.81 (SCS for the whole sample), 2.81 (SCS for minor pathogens), and 3.33 (SCS for major pathogens). These correctly classified, respectively, 69.0%, 73.5%, 72.6% and 91.0% of infected udders in the samples. The CMT appeared only to discriminate udders infected with major pathogens. In this population, SCS appeared to be the best indirect test of the bacteriological status of the udder. PMID:23317658
International Nuclear Information System (INIS)
To investigate serum carcinoembryonic antigen (CEA) as a prognostic factor for rectal cancer patients receiving pre-operative chemoradiotherapy (CRT). Between 2000 and 2009, 138 patients with advanced rectal cancer receiving CRT before surgery at our hospital were retrospectively classified into 3 groups: pre-CRT CEA <6 ng/ml (group L; n = 87); pre-CRT CEA ≥ 6 ng/ml and post-CRT CEA <6 ng/ml (group H-L; n = 32); and both pre- and post-CRT CEA ≥ 6 ng/ml (group H-H; n = 19). CEA ratio (defined as post-CRT CEA divided by pre-CRT CEA), post-CRT CEA level and other factors were reviewed for prediction of pathologic complete response (pCR). Five-year disease-free survival (DFS) was better in groups L (69.0%) and H-L (74.5%) than in group H-H (44.9%) (p = 0.024). Pathologic complete response was observed in 19.5%, 21.9% and 5.3% of groups L, H-L and H-H respectively (p = 0.281). Multivariate analysis showed that ypN stage and pCR were independent prognostic factors for DFS and that post-CRT CEA level was independently predictive of pCR. As a whole, post-CRT CEA <2.61 ng/ml predicted pCR (sensitivity 76.0%; specificity 58.4%). For those with pre-CRT CEA ≥6 ng/ml, post-CRT CEA and CEA ratio both predicted pCR (sensitivity 87.5%, specificity 76.7%). In patients with pre-CRT serum CEA ≥6 ng/ml, those with “normalized” CEA levels after CRT may have similar DFS to those with “normal” (<6 ng/ml) pre-CRT values. Post-CRT CEA level is a predictor for pCR, especially in those with pre-CRT CEA ≥6 ng/ml
Gelman, Andrew; Stern, Hal S; Dunson, David B; Vehtari, Aki; Rubin, Donald B
2013-01-01
FUNDAMENTALS OF BAYESIAN INFERENCEProbability and InferenceSingle-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian ApproachesHierarchical ModelsFUNDAMENTALS OF BAYESIAN DATA ANALYSISModel Checking Evaluating, Comparing, and Expanding ModelsModeling Accounting for Data Collection Decision AnalysisADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional ApproximationsREGRESSION MODELS Introduction to Regression Models Hierarchical Linear
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...
Bayesian Games with Intentions
Bjorndahl, Adam; Halpern, Joseph Y.; Pass, Rafael
2016-01-01
We show that standard Bayesian games cannot represent the full spectrum of belief-dependent preferences. However, by introducing a fundamental distinction between intended and actual strategies, we remove this limitation. We define Bayesian games with intentions, generalizing both Bayesian games and psychological games, and prove that Nash equilibria in psychological games correspond to a special class of equilibria as defined in our setting.
Directory of Open Access Journals (Sweden)
Christian Ifedili Okafor
2011-01-01
Full Text Available Objective: To compare the performance of waist circumference (WC and waist-to-hip ratio (WHR in predicting the presence of cardiovascular risk factors (hypertension and generalized obesity in an apparently healthy population. Materials and Methods: We recruited 898 apparently healthy subjects (318 males and 580 females of the Igbo ethnic group resident in Enugu (urban, Southeast Nigeria. Data collection was done using the World Health Organization Stepwise approach to Surveillance of risk factors (STEPS instrument. Subjects had their weight, height, waist and hip circumferences, systolic and diastolic blood pressures measured according to the guidelines in the step 2 of STEPS instrument. Generalized obesity and hypertension were defined using body mass index (BMI and JNC 7 classifications, respectively. Quantitative and qualitative variables were analyzed using t-test and Chi-square analysis, respectively, while the performance of WC and WHR was compared using the Receiver Operating Characteristic (ROC analysis. P value was set at <0.05. Results: The mean age of the subjects was 48.7 (12.9 years. Central obesity was found in 76.9% and 66.5% of subjects using WHR and WC, respectively. WC had a significantly higher area under the curve (AUC than WHR in all the cardiovascular risk groups, namely, generalized obesity (AUC = 0.88 vs. 0.62, hypertension alone (AUC = 0.60 vs. 0.53, and both generalized obesity and hypertension (AUC = 0.86 vs. 0.57. Conclusion: WC performed better than WHR in predicting the presence of cardiovascular risk factors. Being a simple index, it can easily be measured in routine clinic settings without the need for calculations or use of cumbersome techniques.
Emir Kamenica; Matthew Gentzkow
2011-01-01
When is it possible for one person to persuade another to change her action? We consider a symmetric information model where a sender chooses a signal to reveal to a receiver, who then takes a noncontractible action that affects the welfare of both players. We derive necessary and sufficient conditions for the existence of a signal that strictly benefits the sender. We characterize sender-optimal signals. We examine comparative statics with respect to the alignment of the sender's and the rec...
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.
Bayesian Soft Sensing in Cold Sheet Rolling
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Jirsa, Ladislav
Praha: ÚTIA AV ČR, v.v.i, 2010. s. 45-45. [6th International Workshop on Data–Algorithms–Decision Making. 2.12.2010-4.12.2010, Jindřichův Hradec] R&D Projects: GA MŠk(CZ) 7D09008 Institutional research plan: CEZ:AV0Z10750506 Keywords : soft sensor * bayesian statistics * bayesian model averaging Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/AS/dedecius-bayesian soft sensing in cold sheet rolling.pdf
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
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
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
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...... settings, where cues shape expectations about a small number of upcoming stimuli and thus convey "prior" information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its......The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of...
Eickhoff, Martin; Meyer-Grünefeldt, Mirko; Keller, Lothar
2016-05-01
Nowadays molten salt is efficiently used in point concentrating solar thermal power plants. Line focusing systems still have the disadvantage of elevated heat losses at night because of active freeze protection of the solar field piping system. In order to achieve an efficient operation of line focusing solar power plants using molten salt, a new plant design and a novel operating strategy is developed for Linear Fresnel- and Parabolic Trough power plants. Daily vespertine drainage of the solar field piping and daily matutinal refilling of the solar preheated absorber tubes eliminate the need of nocturnal heating of the solar field and reduce nocturnal heat losses to a minimum. The feasibility of this new operating strategy with all its sub-steps has been demonstrated experimentally.
Frühwirth-Schnatter, Sylvia
1990-01-01
In the paper at hand we apply it to Bayesian statistics to obtain "Fuzzy Bayesian Inference". In the subsequent sections we will discuss a fuzzy valued likelihood function, Bayes' theorem for both fuzzy data and fuzzy priors, a fuzzy Bayes' estimator, fuzzy predictive densities and distributions, and fuzzy H.P.D .-Regions. (author's abstract)
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…
Pasanen, P. A.; Eskelinen, M.; Partanen, K.; Pikkarainen, P; Penttilä, I.; Alhava, E
1993-01-01
The serum values of the tumour markers carcinoembryonic antigen (CEA), cancer-associated carboanhydrate antigens CA 50 and CA 242 were evaluated in 193 patients with hepatopancreato-biliary diseases by receiver operating characteristic (ROC) curve analysis in order to compare their diagnostic accuracy in pancreatic cancer (n = 26), and to define optimal cut-off levels for the serum values of these tumour markers in the diagnosis of pancreatic cancer. The ROC analysis showed that all marker te...
Koen, Joshua D.; Yonelinas, Andrew P.
2011-01-01
Receiver operating characteristics (ROCs) have been used extensively to study the processes underlying human recognition memory, and this method has recently been applied in studies of rats. However, the extent to which the results from human and animal studies converge is neither entirely clear, nor is it known how the different methods used to obtain ROCs in different species impact the results. A recent study used a response bias ROC manipulation with rats and demonstrated that speeding me...
International Nuclear Information System (INIS)
This paper rises methodological questions concerning Free-Response Receiver Operating Characteristic (FROC) analysis as used to judge the quality of images in radiology. This paper has three purposes: describing FROC analysis as used in the literature; discussing whether FROC methods achieve their objectives; and identifying several methodological shortcomings in FROC at the present state of the art that must be solved if FROC is to be fully useful
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
inference algorithms based on the proposed prior representation for sparse channel estimation in orthogonal frequency-division multiplexing receivers. The inference algorithms, which are mainly obtained from variational Bayesian methods, exploit the underlying sparse structure of wireless channel responses......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 of...
Energy Technology Data Exchange (ETDEWEB)
Khivsara, Sagar [Indian Institute of Science, Bangalor (India)
2015-01-01
Recent studies have evaluated closed-loop supercritical carbon dioxide (s-CO_{2}) Brayton cycles to be a higher energy-density system in comparison to conventional superheated steam Rankine systems. At turbine inlet conditions of 923K and 25 MPa, high thermal efficiency (~50%) can be achieved. Achieving these high efficiencies will make concentrating solar power (CSP) technologies a competitive alternative to current power generation methods. To incorporate a s-CO_{2} Brayton power cycle in a solar power tower system, the development of a solar receiver capable of providing an outlet temperature of 923 K (at 25 MPa) is necessary. To satisfy the temperature requirements of a s-CO_{2} Brayton cycle with recuperation and recompression, it is required to heat s-CO_{2 }by a temperature of ~200 K as it passes through the solar receiver. Our objective was to develop an optical-thermal-fluid model to design and evaluate a tubular receiver that will receive a heat input ~1 MWth from a heliostat field. We also undertook the documentation of design requirements for the development, testing and safe operation of a direct s-CO_{2} solar receiver. The main purpose of this document is to serve as a reference and guideline for design and testing requirements, as well as to address the technical challenges and provide initial parameters for the computational models that will be employed for the development of s-CO_{2} receivers.
Granade, Christopher; Cory, D G
2015-01-01
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of- the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we solve all three problems. First, we use modern statistical methods, as pioneered by Husz\\'ar and Houlsby and by Ferrie, to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first informative priors on quantum states and channels. Finally, we develop a method that allows online tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.
Bayesian exploratory factor analysis
Gabriella Conti; Sylvia Frühwirth-Schnatter; James Heckman; Rémi Piatek
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identifi cation criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study c...
Bayesian Exploratory Factor Analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study co...
Bayesian Exploratory Factor Analysis
Gabriella Conti; Sylvia Fruehwirth-Schnatter; Heckman, James J.; Remi Piatek
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on \\emph{ad hoc} classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo s...
Bayesian exploratory factor analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo st...
Bayesian exploratory factor analysis
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James; Piatek, Rémi
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study co...
Carbonetto, Peter; Kisynski, Jacek; De Freitas, Nando; Poole, David L
2012-01-01
The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the Bayesian literature. We introduce syntax for reasoning about arbitrary collections of objects, and their properties, in an intuitive manner. By expl...
Bayesian default probability models
Andrlíková, Petra
2014-01-01
This paper proposes a methodology for default probability estimation for low default portfolios, where the statistical inference may become troublesome. The author suggests using logistic regression models with the Bayesian estimation of parameters. The piecewise logistic regression model and Box-Cox transformation of credit risk score is used to derive the estimates of probability of default, which extends the work by Neagu et al. (2009). The paper shows that the Bayesian models are more acc...
Collaborative Kalman Filtration: Bayesian Perspective
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
Lisabon, Portugalsko: Institute for Systems and Technologies of Information, Control and Communication (INSTICC), 2014, s. 468-474. ISBN 978-989-758-039-0. [11th International Conference on Informatics in Control, Automation and Robotics - ICINCO 2014. Vien (AT), 01.09.2014-03.09.2014] R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : Bayesian analysis * Kalman filter * distributed estimation Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431324.pdf
Directory of Open Access Journals (Sweden)
X. Chen
2013-09-01
Full Text Available A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.
Nutz, Sabine; Döll, Katharina; Karlovsky, Petr
2011-01-01
Real-time PCR (qPCR) is the principal technique for the quantification of pathogen biomass in host tissue, yet no generic methods exist for the determination of the limit of quantification (LOQ) and the limit of detection (LOD) in qPCR. We suggest using the Youden index in the context of the receiver operating characteristic (ROC) curve analysis for this purpose. The LOQ was defined as the amount of target DNA that maximizes the sum of sensitivity and specificity. The LOD was defined as the l...
Bayesian Test of Significance for Conditional Independence: The Multinomial Model
de Morais Andrade, Pablo; Stern, Julio; de Bragança Pereira, Carlos
2014-03-01
Conditional independence tests (CI tests) have received special attention lately in Machine Learning and Computational Intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of Probabilistic Graphical Models (PGM)--which includes Bayesian Networks (BN) models--CI tests are especially important for the task of learning the PGM structure from data. In this paper, we propose the Full Bayesian Significance Test (FBST) for tests of conditional independence for discrete datasets. FBST is a powerful Bayesian test for precise hypothesis, as an alternative to frequentist's significance tests (characterized by the calculation of the \\emph{p-value}).
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. PMID:27375438
Wang, Charles C.; Sue, Miles K.; Manshadi, Farzin; Kinman, Peter
2005-01-01
This paper will first describe the characteristics of interference from a typical EESS satellite, including the intensity, frequency and duration of such interference. The paper will then discuss the DSN interference susceptibility, including the various components in the receiving systems that are susceptible to interference and the recovery time after a strong interference. Finally, the paper will discuss the impact of interference on science data and missions operations.
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
DEFF Research Database (Denmark)
Gardner, Ian A.; Greiner, Matthias
2006-01-01
Receiver-operating characteristic (ROC) curves provide a cutoff-independent method for the evaluation of continuous or ordinal tests used in clinical pathology laboratories. The area under the curve is a useful overall measure of test accuracy and can be used to compare different tests (or differ...... calculated as the slope (tangent) to the ROC curve at a unique test value. We use ROC analysis and calculate likelihood ratios to evaluate the performance of tests reported in 2 articles previously published in this journal....... specificity in test evaluation studies. In addition, calculation of likelihood ratios can potentially improve the clinical utility of such studies because likelihood ratios provide an indication of how the post-test probability changes as a function of the magnitude of the test results. For ordinal test......Receiver-operating characteristic (ROC) curves provide a cutoff-independent method for the evaluation of continuous or ordinal tests used in clinical pathology laboratories. The area under the curve is a useful overall measure of test accuracy and can be used to compare different tests (or...
International Nuclear Information System (INIS)
Highlights: ► A 15 kW solar chemical receiver/reactor for hydrogen production was developed. ► The solar thermochemical efficiency of the receiver/reactor was in the range of 20–28%. ► Hydrogen production exceeding 80% was achieved. ► The research results extend the application of mid-temperature solar thermal energy. -- Abstract: In this paper, we report the operational performance and energy conversion efficiency of a developed 15 kW solar chemical receiver/reactor for hydrogen production. A concentrated solar heat of around 200–300 °C was utilized to provide process heat to drive methanol steam reforming. A modified 15 kW direct-irradiation solar reactor coupled with a linear receiver positioned along the focal line of a one-axis parabolic trough concentrator was used. The experiments were conducted from 200 to 300 °C under a mean solar flux of 300–800 W/m2 and a reactant feeding rate of 6 kg/h. Reactants were continuously fed, and the attained conversion rate of methanol was more than 70% at 700 W/m2. The typical solar thermochemical efficiency of solar thermal energy converted into chemical energy was in the 20–28% range. The overall energy efficiency of input solar power conversion into chemical energy reached up to 17% and may be further increased by improving solar field efficiency. Hydrogen production exceeding 80% was achieved. In addition, preliminary economic evaluation was performed, and methods for further improvement were proposed. This paper proves that solar hydrogen production is feasible by combining solar thermal energy with alternative fuel at around 200–300 °C, which is much lower than the temperature of other solar thermochemical processes. This may offer an economic approach to solar fuel production and extend the application of mid-temperature solar thermal energy.
Computationally efficient Bayesian inference for inverse problems.
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.
2007-10-01
Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.
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
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
Directory of Open Access Journals (Sweden)
Alejandro Jara
2011-04-01
Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
Bayesian variable selection for detecting adaptive genomic differences among populations.
Riebler, Andrea; Held, Leonhard; Stephan, Wolfgang
2008-03-01
We extend an F(st)-based Bayesian hierarchical model, implemented via Markov chain Monte Carlo, for the detection of loci that might be subject to positive selection. This model divides the F(st)-influencing factors into locus-specific effects, population-specific effects, and effects that are specific for the locus in combination with the population. We introduce a Bayesian auxiliary variable for each locus effect to automatically select nonneutral locus effects. As a by-product, the efficiency of the original approach is improved by using a reparameterization of the model. The statistical power of the extended algorithm is assessed with simulated data sets from a Wright-Fisher model with migration. We find that the inclusion of model selection suggests a clear improvement in discrimination as measured by the area under the receiver operating characteristic (ROC) curve. Additionally, we illustrate and discuss the quality of the newly developed method on the basis of an allozyme data set of the fruit fly Drosophila melanogaster and a sequence data set of the wild tomato Solanum chilense. For data sets with small sample sizes, high mutation rates, and/or long sequences, however, methods based on nucleotide statistics should be preferred. PMID:18245358
Bayesian least squares deconvolution
Asensio Ramos, A.; Petit, P.
2015-11-01
Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Bayesian least squares deconvolution
Ramos, A Asensio
2015-01-01
Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the Bayesian framework and we introduce a flexible Gaussian Process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results. We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Loredo, T J
2004-01-01
I describe a framework for adaptive scientific exploration based on iterating an Observation--Inference--Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data--measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object--show the approach can significantly improve observational eff...
Bankov, V. N.; Barulin, L. G.; Zhodzishskii, M. I.; Malyshev, I. V.; Petrusinskii, V. V.
The book is concerned with the design of microelectronic radio receivers and their components based on semiconductor and hybrid integrated circuits. Topics discussed include the hierarchical structure of radio receivers, the synthesis of structural schemes, the design of the principal functional units, and the design of radio receiver systems with digital signal processing. The discussion also covers the integrated circuits of multifunctional amplifiers, analog multipliers, charge-transfer devices, frequency filters, piezoelectronic devices, and microwave amplifiers, filters, and mixers.
Bayesian and frequentist inequality tests
David M. Kaplan; Zhuo, Longhao
2016-01-01
Bayesian and frequentist criteria are fundamentally different, but often posterior and sampling distributions are asymptotically equivalent (and normal). We compare Bayesian and frequentist hypothesis tests of inequality restrictions in such cases. For finite-dimensional parameters, if the null hypothesis is that the parameter vector lies in a certain half-space, then the Bayesian test has (frequentist) size $\\alpha$; if the null hypothesis is any other convex subspace, then the Bayesian test...
Bayesian multiple target tracking
Streit, Roy L
2013-01-01
This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.;
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...... corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
International Nuclear Information System (INIS)
Receiver operating characteristic (ROC) analysis of left ventricular performance at rest was applied to evaluate diagnostic utility of non-imaging nuclear detector (''Nuclear Stethoscope''), for screening patients with coronary artery disease (CAD). Thirty-one patients without CAD and normal rest and stress radionuclide ventriculography (MUGA) were used as a control group. Another 62 patients with abnormal left ventricular reserve and segmental wall motion abnormalities at rest were also studied. All 93 patients were studied with the Nuclear Stethoscope (30 minutes after conventional MUGA testing) both in beat-to-beat and gated equilibrium modes. ROC analysis showed that along with ejection fraction, stroke and end-diastolic volumes, evaluation of the left ventricular filling phase has a great potential for the identification of patients with a segmental wall motion abnormality and, therefore, significant CAD
Energy Technology Data Exchange (ETDEWEB)
Jeon, Tae Joo; Lee, Jong Doo; Kim, Hee Joung; Kim, Myung Jin; Yoo, Hyung Sik [College of Medicine, Yonsei Univ., Seoul (Korea, Republic of)
2000-07-01
The aims of this study is to validate the usefulness of ordered subset expectation maximization (OSEM) comparing with filtered back projection in terms of diagnostic ability for hepatocellular carcinoma (HCC). The data of fifty seven patients with HCC and 62 patients with normal liver was reconstructed using both OSEM and FBP. Mean age of the patients group was 54.4{+-}1.5 year. All patient underwent whole body and liver scan after the injection of 10 mCi of (F-18)FDG using dedicated whole body PET camera (GE, Advance). Interpretation of PET images were performed by 3 observers with random exposure of normal and diseased cases. Receiver operator characteristic (ROC) study was used for the validation of results. The area of ROC curve. Az was represented as below and this results revealed statistical differences (p<0.05). In PET studies of patients with HCC, OSEM showed better results that those of conventional FBP in terms of lesion detectability.
Message-Passing Receiver for OFDM Systems over Highly Delay-Dispersive Channels
DEFF Research Database (Denmark)
Barbu, Oana-Elena; Manchón, Carles Navarro; Rom, Christian; Fleury, Bernard Henri
2016-01-01
Wireless OFDM receivers operating over channels with maximum excess delay exceeding the duration of the cyclic prefix experience intersymbol and intercarrier interference. When not accounted for, this interference impairs the ability of the receiver to estimate the channel response and thereby...... severely limits its equalization and detection capabilities. As a result, the bit-error-rate performance is drastically impaired. In this paper, we propose a receiver algorithm designed to cope with the interference resulting in these channel conditions. The algorithm works with blocks of received OFDM...... contain a few non-negligible multipath components, channel estimation is implemented using a sparse Bayesian learning method. This approach allows for the design of accurate channel estimators with affordable complexity. Results from Monte Carlo simulations show that the receiver employing the proposed...
International Nuclear Information System (INIS)
Purpose: From independently conducted free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) experiments, to study fixed-reader associations between three estimators: the area under the alternative FROC (AFROC) curve computed from FROC data, the area under the ROC curve computed from FROC highest rating data, and the area under the ROC curve computed from confidence-of-disease ratings. Methods: Two hundred mammograms, 100 of which were abnormal, were processed by two image-processing algorithms and interpreted by four radiologists under the FROC paradigm. From the FROC data, inferred-ROC data were derived, using the highest rating assumption. Eighteen months afterwards, the images were interpreted by the same radiologists under the conventional ROC paradigm; conventional-ROC data (in contrast to inferred-ROC data) were obtained. FROC and ROC (inferred, conventional) data were analyzed using the nonparametric area-under-the-curve (AUC), (AFROC and ROC curve, respectively). Pearson correlation was used to quantify the degree of association between the modality-specific AUC indices and standard errors were computed using the bootstrap-after-bootstrap method. The magnitude of the correlations was assessed by comparison with computed Obuchowski-Rockette fixed reader correlations. Results: Average Pearson correlations (with 95% confidence intervals in square brackets) were: Corr(FROC, inferred ROC) = 0.76[0.64, 0.84] > Corr(inferred ROC, conventional ROC) = 0.40[0.18, 0.58] > Corr (FROC, conventional ROC) = 0.32[0.16, 0.46]. Conclusions: Correlation between FROC and inferred-ROC data AUC estimates was high. Correlation between inferred- and conventional-ROC AUC was similar to the correlation between two modalities for a single reader using one estimation method, suggesting that the highest rating assumption might be questionable.
Thomas, Claudine
1994-01-01
The Global Positioning System is an outstanding tool for the dissemination of time. Using mono-channel C/A-code GPS time receivers, the restitution of GPS time through the satellite constellation presents a peak-to-peak discrepancy of several tens of nanoseconds without SA but may be as high as several hundreds of nanoseconds with SA. As a consequence, civil users are more and more interested in implementing hardware and software methods for efficient restitution of GPS time, especially in the framework of the project of a real-time prediction of UTC (UTCp) which could be available in the form of time differences (UTCp - GPS time). Previous work, for improving the real-time restitution of GPS time with SA, to the level obtained without SA, focused on the implementation of a Kalman filter based on past data and updated at each new observation. An alternative solution relies upon the statistical features of the noise brought about by SA; it has already been shown that the SA noise is efficiently reduced by averaging data from numerous satellites observed simultaneously over a sufficiently long time. This method was successfully applied to data from a GPS time receiver, model AOA TTR-4P, connected to the cesium clock kept at the BIPM. This device, a multi-channel, dual frequency, P-code GPS time receiver, is one of the first TTR-4P units in operation in a civil laboratory. Preliminary comparative studies of this new equipment with conventional GPS time receivers are described in this paper. The results of an experimental restitution of GPS time, obtained in June 1993, are also detailed: 3 to 6 satellites were observed simultaneously with a sample interval of 15 s, an efficient smoothing of SA noise was realized by averaging data on all observed satellites over more than 1 hour. When the GPS system is complete in 1994, 8 satellites will be observable continuously from anywhere in the world and the same level of uncertainty will be obtained using a shorter averaging
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...
Czech Academy of Sciences Publication Activity Database
Krejsa, Jiří; Věchet, S.
Bratislava: Slovak University of Technology in Bratislava, 2010, s. 217-222. ISBN 978-80-227-3353-3. [Robotics in Education . Bratislava (SK), 16.09.2010-17.09.2010] Institutional research plan: CEZ:AV0Z20760514 Keywords : mobile robot localization * bearing only beacons * Bayesian filters Subject RIV: JD - Computer Applications, Robotics
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.;
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimenta...
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
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 as a...
Loredo, Thomas J.
2004-04-01
I describe a framework for adaptive scientific exploration based on iterating an Observation-Inference-Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data-measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object-show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable tool for optimizing scientific exploration.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Accelerated aging tests on ENEA-ASE solar coating for receiver tube suitable to operate up to 550 °C
Antonaia, A.; D'Angelo, A.; Esposito, S.; Addonizio, M. L.; Castaldo, A.; Ferrara, M.; Guglielmo, A.; Maccari, A.
2016-05-01
A patented solar coating for evacuated receiver, based on innovative graded WN-AlN cermet layer, has been optically designed and optimized to operate at high temperature with high performance and high thermal stability. This solar coating, being designed to operate in solar field with molten salt as heat transfer fluid, has to be thermally stable up to the maximum temperature of 550 °C. With the aim of determining degradation behaviour and lifetime prediction of the solar coating, we chose to monitor the variation of the solar absorptance αs after each thermal annealing cycle carried out at accelerated temperatures under vacuum. This prediction method was coupled with a preliminary Differential Thermal Analysis (DTA) in order to give evidence for any chemical-physical coating modification in the temperature range of interest before performing accelerated aging tests. In the accelerated aging tests we assumed that the temperature dependence of the degradation processes could be described by Arrhenius behaviour and we hypothesized that a linear correlation occurs between optical parameter variation rate (specifically, Δαs/Δt) and degradation process rate. Starting from Δαs/Δt values evaluated at 650 and 690 °C, Arrhenius plot gave an activation energy of 325 kJ mol-1 for the degradation phenomenon, where the prediction on the coating degradation gave a solar absorptance decrease of only 1.65 % after 25 years at 550 °C. This very low αs decrease gave evidence for an excellent stability of our solar coating, also when employed at the maximum temperature (550 °C) of a solar field operating with molten salt as heat transfer fluid.
Quantum Bayesianism at the Perimeter
Fuchs, Christopher A
2010-01-01
The author summarizes the Quantum Bayesian viewpoint of quantum mechanics, developed originally by C. M. Caves, R. Schack, and himself. It is a view crucially dependent upon the tools of quantum information theory. Work at the Perimeter Institute for Theoretical Physics continues the development and is focused on the hard technical problem of a finding a good representation of quantum mechanics purely in terms of probabilities, without amplitudes or Hilbert-space operators. The best candidate representation involves a mysterious entity called a symmetric informationally complete quantum measurement. Contemplation of it gives a way of thinking of the Born Rule as an addition to the rules of probability theory, applicable when one gambles on the consequences of interactions with physical systems. The article ends by outlining some directions for future work.
On Bayesian System Reliability Analysis
International Nuclear Information System (INIS)
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
Bayesian Magic in Asteroseismology
Kallinger, T.
2015-09-01
Only a few years ago asteroseismic observations were so rare that scientists had plenty of time to work on individual data sets. They could tune their algorithms in any possible way to squeeze out the last bit of information. Nowadays this is impossible. With missions like MOST, CoRoT, and Kepler we basically drown in new data every day. To handle this in a sufficient way statistical methods become more and more important. This is why Bayesian techniques started their triumph march across asteroseismology. I will go with you on a journey through Bayesian Magic Land, that brings us to the sea of granulation background, the forest of peakbagging, and the stony alley of model comparison.
Bayesian Nonparametric Graph Clustering
Banerjee, Sayantan; Akbani, Rehan; Baladandayuthapani, Veerabhadran
2015-01-01
We present clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables. As opposed to standard approaches that assume known graph structures, we first estimate the edge structure of the unknown graph using Bayesian neighborhood selection approaches, wherein we account for the uncertainty of graphical structure learning through model-averaged estimates of the suitable parameters. Subsequently, we develop a nonparametric graph cluster...
Bayesian Benchmark Dose Analysis
Fang, Qijun; Piegorsch, Walter W.; Barnes, Katherine Y.
2014-01-01
An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs) that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). Appeal to Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indee...
Bayesian Generalized Rating Curves
Helgi Sigurðarson 1985
2014-01-01
A rating curve is a curve or a model that describes the relationship between water elevation, or stage, and discharge in an observation site in a river. The rating curve is fit from paired observations of stage and discharge. The rating curve then predicts discharge given observations of stage and this methodology is applied as stage is substantially easier to directly observe than discharge. In this thesis a statistical rating curve model is proposed working within the framework of Bayesian...
Heteroscedastic Treed Bayesian Optimisation
Assael, John-Alexander M.; Wang, Ziyu; Shahriari, Bobak; De Freitas, Nando
2014-01-01
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions. However, in many cases a single Gaussian process is not flexible enough to capture non-stat...
Efficient Bayesian Phase Estimation
Wiebe, Nathan; Granade, Chris
2016-07-01
We introduce a new method called rejection filtering that we use to perform adaptive Bayesian phase estimation. Our approach has several advantages: it is classically efficient, easy to implement, achieves Heisenberg limited scaling, resists depolarizing noise, tracks time-dependent eigenstates, recovers from failures, and can be run on a field programmable gate array. It also outperforms existing iterative phase estimation algorithms such as Kitaev's method.
Brody, Samuel; Lapata, Mirella
2009-01-01
Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word’s contexts into different classes, each representing a word sense. Our work places sense induction in a Bayesian context by modeling the contexts of the ambiguous word as samp...
Bayesian Neural Word Embedding
Barkan, Oren
2016-01-01
Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-gram (SG) with negative sampling, known also as Word2Vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm that can be beneficial to general item similarity tasks as well. The algorithm relies on a Variational Bayes solution for the SG objective and a detailed step by ...
Directory of Open Access Journals (Sweden)
Bhaskar Bhattacharya
2013-04-01
Full Text Available Receiver operating characteristic (ROC curves have application in analysis of the performance of diagnostic indicators used in the assessment of disease risk in clinical and veterinary medicine and in crop protection. For a binary indicator, an ROC curve summarizes the two distributions of risk scores obtained by retrospectively categorizing subjects as cases or controls using a gold standard. An ROC curve may be symmetric about the negative diagonal of the graphical plot, or skewed towards the left-hand axis or the upper axis of the plot. ROC curves with different symmetry properties may have the same area under the curve. Here, we characterize the symmetry properties of bi-Normal and bi-gamma ROC curves in terms of the Kullback-Leibler divergences (KLDs between the case and control distributions of risk scores. The KLDs describe the known symmetry properties of bi-Normal ROC curves, and newly characterize the symmetry properties of constant-shape and constant-scale bi-gamma ROC curves. It is also of interest to note an application of KLDs where their asymmetry—often an inconvenience—has a useful interpretation.
Chen, Yuh-Ing; Huang, Chi-Shen; Liu, Jann-Yenq
2015-12-01
Evidence of the seismo-ionospheric precursor (SIP) is reported by statistically investigating the relationship between the total electron content (TEC) in global ionosphere map (GIM) and 56 M ⩾ 6.0 earthquakes during 1998-2013 in China. A median-based method together with the z test is employed to examine the TEC variations 30 days before and after the earthquake. It is found that the TEC significantly decreases 0600-1000 LT 1-6 days before the earthquake, and anomalously increases in 3 time periods of 1300-1700 LT 12-15 days; 0000-0500 LT 15-17 days; and 0500-0900 LT 22-28 days before the earthquake. The receiver operating characteristic (ROC) curve is then used to evaluate the efficiency of TEC for predicting M ⩾ 6.0 earthquakes in China during a specified time period. Statistical results suggest that the SIP is the significant TEC reduction in the morning period of 0600-1000 LT. The SIP is further confirmed since the area under the ROC curve is positively associated with the earthquake magnitude.
Directory of Open Access Journals (Sweden)
Provvidenza M. Abruzzo
2015-01-01
Full Text Available Autism Spectrum Disorders (ASD are a heterogeneous group of neurodevelopmental disorders. Recognized causes of ASD include genetic factors, metabolic diseases, toxic and environmental factors, and a combination of these. Available tests fail to recognize genetic abnormalities in about 70% of ASD children, where diagnosis is solely based on behavioral signs and symptoms, which are difficult to evaluate in very young children. Although it is advisable that specific psychotherapeutic and pedagogic interventions are initiated as early as possible, early diagnosis is hampered by the lack of nongenetic specific biological markers. In the past ten years, the scientific literature has reported dozens of neurophysiological and biochemical alterations in ASD children; however no real biomarker has emerged. Such literature is here reviewed in the light of Receiver Operating Characteristic (ROC analysis, a very valuable statistical tool, which evaluates the sensitivity and the specificity of biomarkers to be used in diagnostic decision making. We also apply ROC analysis to some of our previously published data and discuss the increased diagnostic value of combining more variables in one ROC curve analysis. We also discuss the use of biomarkers as a tool for advancing our understanding of nonsyndromic ASD.
International Nuclear Information System (INIS)
The objectives are to describe the disadvantages of the area under the receiver operating characteristic curve (ROC AUC) to measure diagnostic test performance and to propose an alternative based on net benefit. We use a narrative review supplemented by data from a study of computer-assisted detection for CT colonography. We identified problems with ROC AUC. Confidence scoring by readers was highly non-normal, and score distribution was bimodal. Consequently, ROC curves were highly extrapolated with AUC mostly dependent on areas without patient data. AUC depended on the method used for curve fitting. ROC AUC does not account for prevalence or different misclassification costs arising from false-negative and false-positive diagnoses. Change in ROC AUC has little direct clinical meaning for clinicians. An alternative analysis based on net benefit is proposed, based on the change in sensitivity and specificity at clinically relevant thresholds. Net benefit incorporates estimates of prevalence and misclassification costs, and it is clinically interpretable since it reflects changes in correct and incorrect diagnoses when a new diagnostic test is introduced. ROC AUC is most useful in the early stages of test assessment whereas methods based on net benefit are more useful to assess radiological tests where the clinical context is known. Net benefit is more useful for assessing clinical impact. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Halligan, Steve [University College London, Centre for Medical Imaging, University College Hospital, London (United Kingdom); Altman, Douglas G. [University of Oxford, Centre for Statistics in Medicine, Oxford (United Kingdom); Mallett, Susan [University of Oxford, Department of Primary Care Health Sciences, Oxford (United Kingdom)
2015-04-01
The objectives are to describe the disadvantages of the area under the receiver operating characteristic curve (ROC AUC) to measure diagnostic test performance and to propose an alternative based on net benefit. We use a narrative review supplemented by data from a study of computer-assisted detection for CT colonography. We identified problems with ROC AUC. Confidence scoring by readers was highly non-normal, and score distribution was bimodal. Consequently, ROC curves were highly extrapolated with AUC mostly dependent on areas without patient data. AUC depended on the method used for curve fitting. ROC AUC does not account for prevalence or different misclassification costs arising from false-negative and false-positive diagnoses. Change in ROC AUC has little direct clinical meaning for clinicians. An alternative analysis based on net benefit is proposed, based on the change in sensitivity and specificity at clinically relevant thresholds. Net benefit incorporates estimates of prevalence and misclassification costs, and it is clinically interpretable since it reflects changes in correct and incorrect diagnoses when a new diagnostic test is introduced. ROC AUC is most useful in the early stages of test assessment whereas methods based on net benefit are more useful to assess radiological tests where the clinical context is known. Net benefit is more useful for assessing clinical impact. (orig.)
Wiegerinck, Wim; Schoenaker, Christiaan; Duane, Gregory
2016-04-01
Recently, methods for model fusion by dynamically combining model components in an interactive ensemble have been proposed. In these proposals, fusion parameters have to be learned from data. One can view these systems as parametrized dynamical systems. We address the question of learnability of dynamical systems with respect to both short term (vector field) and long term (attractor) behavior. In particular we are interested in learning in the imperfect model class setting, in which the ground truth has a higher complexity than the models, e.g. due to unresolved scales. We take a Bayesian point of view and we define a joint log-likelihood that consists of two terms, one is the vector field error and the other is the attractor error, for which we take the L1 distance between the stationary distributions of the model and the assumed ground truth. In the context of linear models (like so-called weighted supermodels), and assuming a Gaussian error model in the vector fields, vector field learning leads to a tractable Gaussian solution. This solution can then be used as a prior for the next step, Bayesian attractor learning, in which the attractor error is used as a log-likelihood term. Bayesian attractor learning is implemented by elliptical slice sampling, a sampling method for systems with a Gaussian prior and a non Gaussian likelihood. Simulations with a partially observed driven Lorenz 63 system illustrate the approach.
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...
Unbounded Bayesian Optimization via Regularization
Shahriari, Bobak; Bouchard-Côté, Alexandre; De Freitas, Nando
2015-01-01
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning. Currently, the established Bayesian optimization practice requires a user-defined bounding box which is assumed to contain the optimizer. However, when little is known about the probed objective function, it can be difficult to prescribe such bounds. In this work we modify the standard Bayesian optimization framework in a principled way to allow automatic resizing of t...
Bayesian optimization for materials design
Frazier, Peter I.; Wang, Jialei
2015-01-01
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian pro...
An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit
Wong, Rowena Syn Yin; Ismail, Noor Azina
2016-01-01
Background and Objectives There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. Methods This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. Results The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. Conclusion Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of
Implementation of an Adaptive Learning System Using a Bayesian Network
Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki
2015-01-01
An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…
Highly Sensitive Photonic Crystal Cavity Laser Noise Measurements using Bayesian Filtering
DEFF Research Database (Denmark)
Piels, Molly; Xue, Weiqi; Schäffer, Christian G.;
2015-01-01
We measure for the first time the frequency noise spectrum of a photonic crystal cavity laser with less than 20 nW of fiber-coupled output power using a coherent receiver and Bayesian filtering.......We measure for the first time the frequency noise spectrum of a photonic crystal cavity laser with less than 20 nW of fiber-coupled output power using a coherent receiver and Bayesian filtering....
Directory of Open Access Journals (Sweden)
Yatan Pal Singh Balhara
2013-01-01
Full Text Available Background: Tobacco use has been associated with various carcinomas including lung, esophagus, larynx, mouth, throat, kidney, bladder, pancreas, stomach, and cervix. Biomarkers such as concentration of cotinine in the blood, urine, or saliva have been used as objective measures to distinguish nonusers and users of tobacco products. A change in the cut-off value of urinary cotinine to detect active tobacco use is associated with a change in sensitivity and sensitivity of detection. Aim: The current study aimed at assessing the impact of using different cut-off thresholds of urinary cotinine on sensitivity and specificity of detection of smoking and smokeless tobacco product use among psychiatric patients. Settings and Design: All the male subjects attending the psychiatry out-patient department of the tertiary care multispecialty teaching hospital constituted the sample frame for the current study in a cross-sectionally. Materials and Methods: Quantitative urinary cotinine assay was done by using ELISA kits of Calbiotech. Inc., USA. We used the receiver operating characteristic (ROC curve to assess the sensitivity and specificity of various cut-off values of urinary cotinine to identify active smokers and users of smokeless tobacco products. Results: ROC analysis of urinary cotinine levels in detection of self-reported smoking provided the area under curve (AUC of 0.434. Similarly, the ROC analysis of urinary cotinine levels in detection of self-reported smoking revealed AUC of 0.44. The highest sensitivity and specificity of 100% for smoking were detected at the urinary cut-off value greater than or equal to 2.47 ng/ml. Conclusions: The choice of cut-off value of urinary cotinine used to distinguish nonusers form active users of tobacco products impacts the sensitivity as well as specificity of detection.
International Nuclear Information System (INIS)
Purpose: The study evaluates the pre- and post-training lesion localisation ability of a group of novice observers. Parallels are drawn with the performance of inexperienced radiographers taking part in preliminary clinical evaluation (PCE) and ‘red-dot’ systems, operating within radiography practice. Materials and methods: Thirty-four novice observers searched 92 images for simulated lesions. Pre-training and post-training evaluations were completed following the free-response the receiver operating characteristic (FROC) method. Training consisted of observer performance methodology, the characteristics of the simulated lesions and information on lesion frequency. Jackknife alternative FROC (JAFROC) and highest rating inferred ROC analyses were performed to evaluate performance difference on lesion-based and case-based decisions. The significance level of the test was set at 0.05 to control the probability of Type I error. Results: JAFROC analysis (F(3,33) = 26.34, p < 0.0001) and highest-rating inferred ROC analysis (F(3,33) = 10.65, p = 0.0026) revealed a statistically significant difference in lesion detection performance. The JAFROC figure-of-merit was 0.563 (95% CI 0.512,0.614) pre-training and 0.677 (95% CI 0.639,0.715) post-training. Highest rating inferred ROC figure-of-merit was 0.728 (95% CI 0.701,0.755) pre-training and 0.772 (95% CI 0.750,0.793) post-training. Conclusions: This study has demonstrated that novice observer performance can improve significantly. This study design may have relevance in the assessment of inexperienced radiographers taking part in PCE or commenting scheme for trauma. - Highlights: • We assess novice observer performance pre- and post-training. • JAFROC and highest rating inferred ROC analyses were completed. • Novice observer performance improved from pre- to post-training. • Novice observers are better at making case-based rather than lesion-based decisions. • Parallels can be drawn between commenting
The NIFTY way of Bayesian signal inference
International Nuclear Information System (INIS)
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
QBism, the Perimeter of Quantum Bayesianism
Fuchs, Christopher A
2010-01-01
This article summarizes the Quantum Bayesian point of view of quantum mechanics, with special emphasis on the view's outer edges---dubbed QBism. QBism has its roots in personalist Bayesian probability theory, is crucially dependent upon the tools of quantum information theory, and most recently, has set out to investigate whether the physical world might be of a type sketched by some false-started philosophies of 100 years ago (pragmatism, pluralism, nonreductionism, and meliorism). Beyond conceptual issues, work at Perimeter Institute is focused on the hard technical problem of finding a good representation of quantum mechanics purely in terms of probabilities, without amplitudes or Hilbert-space operators. The best candidate representation involves a mysterious entity called a symmetric informationally complete quantum measurement. Contemplation of it gives a way of thinking of the Born Rule as an addition to the rules of probability theory, applicable when an agent considers gambling on the consequences of...
Bayesian networks for enterprise risk assessment
Bonafede, C E
2006-01-01
According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. In general risk is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover qualitative data must be converted in numerical values to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Network is a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a Bayesian networks in the parti...
Software Health Management with Bayesian Networks
Mengshoel, Ole; Schumann, JOhann
2011-01-01
Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.
Quantum-like Representation of Bayesian Updating
Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu; Khrennikov, Andrei; Basieva, Irina
2011-03-01
Recently, applications of quantum mechanics to coginitive psychology have been discussed, see [1]-[11]. It was known that statistical data obtained in some experiments of cognitive psychology cannot be described by classical probability model (Kolmogorov's model) [12]-[15]. Quantum probability is one of the most advanced mathematical models for non-classical probability. In the paper of [11], we proposed a quantum-like model describing decision-making process in a two-player game, where we used the generalized quantum formalism based on lifting of density operators [16]. In this paper, we discuss the quantum-like representation of Bayesian inference, which has been used to calculate probabilities for decision making under uncertainty. The uncertainty is described in the form of quantum superposition, and Bayesian updating is explained as a reduction of state by quantum measurement.
Distributed Detection via Bayesian Updates and Consensus
Liu, Qipeng; Wang, Xiaofan
2014-01-01
In this paper, we discuss a class of distributed detection algorithms which can be viewed as implementations of Bayes' law in distributed settings. Some of the algorithms are proposed in the literature most recently, and others are first developed in this paper. The common feature of these algorithms is that they all combine (i) certain kinds of consensus protocols with (ii) Bayesian updates. They are different mainly in the aspect of the type of consensus protocol and the order of the two operations. After discussing their similarities and differences, we compare these distributed algorithms by numerical examples. We focus on the rate at which these algorithms detect the underlying true state of an object. We find that (a) The algorithms with consensus via geometric average is more efficient than that via arithmetic average; (b) The order of consensus aggregation and Bayesian update does not apparently influence the performance of the algorithms; (c) The existence of communication delay dramatically slows do...
Advanced Bayesian Method for Planetary Surface Navigation
Center, Julian
2015-01-01
Autonomous Exploration, Inc., has developed an advanced Bayesian statistical inference method that leverages current computing technology to produce a highly accurate surface navigation system. The method combines dense stereo vision and high-speed optical flow to implement visual odometry (VO) to track faster rover movements. The Bayesian VO technique improves performance by using all image information rather than corner features only. The method determines what can be learned from each image pixel and weighs the information accordingly. This capability improves performance in shadowed areas that yield only low-contrast images. The error characteristics of the visual processing are complementary to those of a low-cost inertial measurement unit (IMU), so the combination of the two capabilities provides highly accurate navigation. The method increases NASA mission productivity by enabling faster rover speed and accuracy. On Earth, the technology will permit operation of robots and autonomous vehicles in areas where the Global Positioning System (GPS) is degraded or unavailable.
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
Computationally efficient Bayesian tracking
Aughenbaugh, Jason; La Cour, Brian
2012-06-01
In this paper, we describe the progress we have achieved in developing a computationally efficient, grid-based Bayesian fusion tracking system. In our approach, the probability surface is represented by a collection of multidimensional polynomials, each computed adaptively on a grid of cells representing state space. Time evolution is performed using a hybrid particle/grid approach and knowledge of the grid structure, while sensor updates use a measurement-based sampling method with a Delaunay triangulation. We present an application of this system to the problem of tracking a submarine target using a field of active and passive sonar buoys.
Improved iterative Bayesian unfolding
D'Agostini, G
2010-01-01
This paper reviews the basic ideas behind a Bayesian unfolding published some years ago and improves their implementation. In particular, uncertainties are now treated at all levels by probability density functions and their propagation is performed by Monte Carlo integration. Thus, small numbers are better handled and the final uncertainty does not rely on the assumption of normality. Theoretical and practical issues concerning the iterative use of the algorithm are also discussed. The new program, implemented in the R language, is freely available, together with sample scripts to play with toy models.
Oliveira, Luis
2015-01-01
This book demonstrates how to design a wideband receiver operating in current mode, in which the noise and non-linearity are reduced, implemented in a low cost single chip, using standard CMOS technology. The authors present a solution to remove the transimpedance amplifier (TIA) block and connect directly the mixer’s output to a passive second-order continuous-time Σ∆ analog to digital converter (ADC), which operates in current-mode. These techniques enable the reduction of area, power consumption, and cost in modern CMOS receivers.
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.
Adaptive Dynamic Bayesian Networks
Energy Technology Data Exchange (ETDEWEB)
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Bayesian analysis toolkit - BAT
International Nuclear Information System (INIS)
Statistical treatment of data is an essential part of any data analysis and interpretation. Different statistical methods and approaches can be used, however the implementation of these approaches is complicated and at times inefficient. The Bayesian analysis toolkit (BAT) is a software package developed in C++ framework that facilitates the statistical analysis of the data using Bayesian theorem. The tool evaluates the posterior probability distributions for models and their parameters using Markov Chain Monte Carlo which in turn provide straightforward parameter estimation, limit setting and uncertainty propagation. Additional algorithms, such as simulated annealing, allow extraction of the global mode of the posterior. BAT sets a well-tested environment for flexible model definition and also includes a set of predefined models for standard statistical problems. The package is interfaced to other software packages commonly used in high energy physics, such as ROOT, Minuit, RooStats and CUBA. We present a general overview of BAT and its algorithms. A few physics examples are shown to introduce the spectrum of its applications. In addition, new developments and features are summarized.
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)
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...... represents the spatial coordinates of the grid nodes. Knowledge of how grid nodes are depicted in the observed image is described through the observation model. The prior consists of a node prior and an arc (edge) prior, both modeled as Gaussian MRFs. The node prior models variations in the positions of grid...... nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing...
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
Institute of Scientific and Technical Information of China (English)
HU Zhao-yong
2005-01-01
Engineering diagnosis is essential to the operation of industrial equipment. The key to successful diagnosis is correct knowledge representation and reasoning. The Bayesian network is a powerful tool for it. This paper utilizes the Bayesian network to represent and reason diagnostic knowledge, named Bayesian diagnostic network. It provides a three-layer topologic structure based on operating conditions, possible faults and corresponding symptoms. The paper also discusses an approximate stochastic sampling algorithm. Then a practical Bayesian network for gas turbine diagnosis is constructed on a platform developed under a Visual C++ environment. It shows that the Bayesian network is a powerful model for representation and reasoning of diagnostic knowledge. The three-layer structure and the approximate algorithm are effective also.
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of...
Portfolio Allocation for Bayesian Optimization
Brochu, Eric; Hoffman, Matthew W.; De Freitas, Nando
2010-01-01
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several differen...
Neuronanatomy, neurology and Bayesian networks
Bielza Lozoya, Maria Concepcion
2014-01-01
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related qualit...
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...
Diffusion filtration with approximate Bayesian computation
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Djurić, P. M.
Piscataway: IEEE Computer Society, 2015, s. 3207-3211. ISBN 978-1-4673-6997-8. ISSN 1520-6149. [2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015). Brisbane (AU), 19.05.2015-24.05.2015] R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : Bayesian filtration * diffusion * distributed filtration Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2015/AS/dedecius-0443931.pdf
Using imsets for learning Bayesian networks
Czech Academy of Sciences Publication Activity Database
Vomlel, Jiří; Studený, Milan
Praha : UTIA AV ČR, 2007 - (Kroupa, T.; Vejnarová, J.), s. 178-189 [Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./. Liblice (CZ), 15.09.2007-18.09.2007] R&D Projects: GA MŠk(CZ) 1M0572 Grant ostatní: GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian networks * artificial intelligence * probabilistic graphical models * machine learning Subject RIV: BB - Applied Statistics, Operational Research
Dale Poirier
2008-01-01
This paper provides Bayesian rationalizations for White’s heteroskedastic consistent (HC) covariance estimator and various modifications of it. An informed Bayesian bootstrap provides the statistical framework.
Bayesian tomographic reconstruction of microsystems
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-11-01
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 nonparametric estimation of hazard rate in monotone Aalen model
Czech Academy of Sciences Publication Activity Database
Timková, Jana
2014-01-01
Roč. 50, č. 6 (2014), s. 849-868. ISSN 0023-5954 Institutional support: RVO:67985556 Keywords : Aalen model * Bayesian estimation * MCMC Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.541, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/timkova-0438210.pdf
Nonparametric Bayesian Classification
Coram, M A
2002-01-01
A Bayesian approach to the classification problem is proposed in which random partitions play a central role. It is argued that the partitioning approach has the capacity to take advantage of a variety of large-scale spatial structures, if they are present in the unknown regression function $f_0$. An idealized one-dimensional problem is considered in detail. The proposed nonparametric prior uses random split points to partition the unit interval into a random number of pieces. This prior is found to provide a consistent estimate of the regression function in the $\\L^p$ topology, for any $1 \\leq p < \\infty$, and for arbitrary measurable $f_0:[0,1] \\rightarrow [0,1]$. A Markov chain Monte Carlo (MCMC) implementation is outlined and analyzed. Simulation experiments are conducted to show that the proposed estimate compares favorably with a variety of conventional estimators. A striking resemblance between the posterior mean estimate and the bagged CART estimate is noted and discussed. For higher dimensions, a ...
BAT - Bayesian Analysis Toolkit
International Nuclear Information System (INIS)
One of the most vital steps in any data analysis is the statistical analysis and comparison with the prediction of a theoretical model. The many uncertainties associated with the theoretical model and the observed data require a robust statistical analysis tool. The Bayesian Analysis Toolkit (BAT) is a powerful statistical analysis software package based on Bayes' Theorem, developed to evaluate the posterior probability distribution for models and their parameters. It implements Markov Chain Monte Carlo to get the full posterior probability distribution that in turn provides a straightforward parameter estimation, limit setting and uncertainty propagation. Additional algorithms, such as Simulated Annealing, allow to evaluate the global mode of the posterior. BAT is developed in C++ and allows for a flexible definition of models. A set of predefined models covering standard statistical cases are also included in BAT. It has been interfaced to other commonly used software packages such as ROOT, Minuit, RooStats and CUBA. An overview of the software and its algorithms is provided along with several physics examples to cover a range of applications of this statistical tool. Future plans, new features and recent developments are briefly discussed.
Bayesian quantum frequency estimation in presence of collective dephasing
International Nuclear Information System (INIS)
We advocate a Bayesian approach to optimal quantum frequency estimation—an important issue for future quantum enhanced atomic clock operation. The approach provides a clear insight into the interplay between decoherence and the extent of prior knowledge in determining the optimal interrogation times and optimal estimation strategies. We propose a general framework capable of describing local oscillator noise as well as additional collective atomic dephasing effects. For a Gaussian noise, the average Bayesian cost can be expressed using the quantum Fisher information. Thus we establish a direct link between the two, often competing, approaches to quantum estimation theory. (paper)
加强应收账款管理 有效防范经营风险%Stepping up Accounts Receivable Management to Prevent Operational Risks
Institute of Scientific and Technical Information of China (English)
余志勇
2012-01-01
For the safety of fund and the prevention of capital loss,those in charge should take the accounting job seriously,divide the job clearly and make sure there are no loopholes in institutions during the pre-warning of accounts receivable.During the mid control phase,they must employ credit limit delivery,credit rating evaluation and risk confirmation in customer service.During the management of accounts receivable,those in charge should seriously determine creditors’ right,step up account checking,clear up the bad loans to minimize the loss and check the acceptance bill to ensure capital safety.%为确保资金安全,防止资产流失,在应收账款的事前预警中,领导应重视、分工应明确、制度应齐全;在应收账款的事中控制中,应实行信用限额发货;推行信用等级评价;强化售后风险确认。在应收账款管理的事后控制中,应认真落实债权,加强对应收款项的对账工作;加大坏账的清欠力度,将损失降到最低;加强承兑汇票查验,保障资金安全。
Advanced solar thermal receiver technology
Kudirka, A. A.; Leibowitz, L. P.
1980-01-01
Development of advanced receiver technology for solar thermal receivers designed for electric power generation or for industrial applications, such as fuels and chemical production or industrial process heat, is described. The development of this technology is focused on receivers that operate from 1000 F to 3000 F and above. Development strategy is mapped in terms of application requirements, and the related system and technical requirements. Receiver performance requirements and current development efforts are covered for five classes of receiver applications: high temperature, advanced Brayton, Stirling, and Rankine cycle engines, and fuels and chemicals.
Bayesian Discovery of Linear Acyclic Causal Models
Hoyer, Patrik O
2012-01-01
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given by linear acyclic causal models (recursive structural equation models). For Gaussian data both constraint-based methods (Spirtes et al., 1993; Pearl, 2000) (which output a single equivalence class) and Bayesian score-based methods (Geiger and Heckerman, 1994) (which assign relative scores to the equivalence classes) are available. On the contrary, all current methods able to utilize non-Gaussianity in the data (Shimizu et al., 2006; Hoyer et al., 2008) always return only a single graph or a single equivalence class, and so are fundamentally unable to express the degree of certainty attached to that output. In this paper we develop a Bayesian score-based approach able to take advantage of non-Gaussianity when estimating linear acyclic causal models, and we empirically demonstrate that, at least on very modest size networks, its accur...
Towards a Supra-Bayesian Approach to Merging of Information
Czech Academy of Sciences Publication Activity Database
Sečkárová, Vladimíra
Prague: Institute of Information Theory and Automation, 2011, s. 81-86. ISBN 978-80-903834-6-3. [The 2nd International Workshop od Decision Making with Multiple Imperfect Decision Makers. Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011). Sierra Nevada (ES), 16.12.2011-16.12.2011] R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : decision makers * Supra-Bayesian * Bayesian solution * Merging Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/AS/seckarova-towards a supra-bayesian approach to merging of information.pdf
FRUITS for Fish: intake estimates of aquatic foods using a novel Bayesian model
Czech Academy of Sciences Publication Activity Database
Fernandes, R.; Brabec, Marek; Millard, A.; Nadeau, J.M.; Grootes, P.M.
University of Ghent, 2013. s. 110-111. [International Symposium 14C & Archaeology /14./. 08.04.2013-12.04.2013, Ghent] Institutional support: RVO:67985807 Keywords : Bayesian estimation * food consuption * archeology Subject RIV: BB - Applied Statistics, Operational Research
Bayesian seismic AVO inversion
Energy Technology Data Exchange (ETDEWEB)
Buland, Arild
2002-07-01
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S
Wang, Huiting; Liu, Renyuan; Zhang, Xin; Li, Ming; Yang, Yongbo; Yan, Jing; Niu, Fengnan; Tian, Chuanshuai; Wang, Kun; Yu, Haiping; Chen, Weibo; Wan, Suiren; Sun, Yu; Zhang, Bing
2016-01-01
Many modalities of magnetic resonance imaging (MRI) have been confirmed to be of great diagnostic value in glioma grading. Contrast enhanced T1-weighted imaging allows the recognition of blood-brain barrier breakdown. Perfusion weighted imaging and MR spectroscopic imaging enable the quantitative measurement of perfusion parameters and metabolic alterations respectively. These modalities can potentially improve the grading process in glioma if combined properly. In this study, Bayesian Network, which is a powerful and flexible method for probabilistic analysis under uncertainty, is used to combine features extracted from contrast enhanced T1-weighted imaging, perfusion weighted imaging and MR spectroscopic imaging. The networks were constructed using K2 algorithm along with manual determination and distribution parameters learned using maximum likelihood estimation. The grading performance was evaluated in a leave-one-out analysis, achieving an overall grading accuracy of 92.86% and an area under the curve of 0.9577 in the receiver operating characteristic analysis given all available features observed in the total 56 patients. Results and discussions show that Bayesian Network is promising in combining features from multiple modalities of MRI for improved grading performance. PMID:27077923
An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power
Directory of Open Access Journals (Sweden)
Antonio Bracale
2015-09-01
Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.
Directory of Open Access Journals (Sweden)
Gaoning He
2010-01-01
Full Text Available A Bayesian game-theoretic model is developed to design and analyze the resource allocation problem in K-user fading multiple access channels (MACs, where the users are assumed to selfishly maximize their average achievable rates with incomplete information about the fading channel gains. In such a game-theoretic study, the central question is whether a Bayesian equilibrium exists, and if so, whether the network operates efficiently at the equilibrium point. We prove that there exists exactly one Bayesian equilibrium in our game. Furthermore, we study the network sum-rate maximization problem by assuming that the users coordinate according to a symmetric strategy profile. This result also serves as an upper bound for the Bayesian equilibrium. Finally, simulation results are provided to show the network efficiency at the unique Bayesian equilibrium and to compare it with other strategies.
Bayesian multivariate mixed-scale density estimation
Canale, Antonio
2011-01-01
Although univariate continuous density estimation has received abundant attention in the Bayesian nonparametrics literature, there is essentially no theory on multivariate mixed scale density estimation. In this article, we consider a general framework to jointly model continuous, count and categorical variables under a nonparametric prior, which is induced through rounding latent variables having an unknown density with respect to Lesbesgue measure. For the proposed class of priors, we provide sufficient conditions for large support, strong consistency and rates of posterior contraction. These conditions, which primarily relate to the prior on the latent variable density and heaviness of the tails for the observed continuous variables, allow one to convert sufficient conditions obtained in the setting of multivariate continuous density estimation to the mixed scale case. We provide new results in the multivariate continuous density estimation case, showing the Kullback-Leibler property and strong consistency...
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 ...
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.
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.
RFI receiver. [deep space network
Lay, R.
1980-01-01
An S-band radio frequency interference (RFI) receiver to analyze and identify sources of RFI problems in the Deep Space Network DSN tracking stations is described. The RFI receiver is a constant gain, double conversion, open loop receiver with dual sine/cosine channel outputs, providing a total of 20 MHZ monitoring capability. This receiver is computer controlled using a MODCOMP II miniprocessor. The RFI receiver has been designed to operate at a 150 Kelvin system noise temperature accomplished by cascading two low noise field effect transistor (FET) amplifiers for the receiver front-end. The first stage low noise FET amplifier is mounted at the feed horn to minimize any cable losses to achieve a lower system noise temperature. The receiver is tunable over the frequency range of 2150 to 2450 MHz in both sine/cosine output channels with a resolution of 100 kHz.
Bayesian test and Kuhn's paradigm
Institute of Scientific and Technical Information of China (English)
Chen Xiaoping
2006-01-01
Kuhn's theory of paradigm reveals a pattern of scientific progress,in which normal science alternates with scientific revolution.But Kuhn underrated too much the function of scientific test in his pattern,because he focuses all his attention on the hypothetico-deductive schema instead of Bayesian schema.This paper employs Bayesian schema to re-examine Kuhn's theory of paradigm,to uncover its logical and rational components,and to illustrate the tensional structure of logic and belief,rationality and irrationality,in the process of scientific revolution.
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 Model Averaging for Propensity Score Analysis
Kaplan, David; Chen, Jianshen
2013-01-01
The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…
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 sup
Ostergaard, Jens
1988-01-01
A medium-size Beacon Receiving System for reception and processing of the B1 (20 GHz) and B2 (30 GHz) beacons from Olympus has been developed. Integration of B1 and B2 receiving equipment into one system using one antenna and a common computer for control and data processing provides the advantages of a compact configuration and synchronization of the two receiver chains. Range for co-polar signal attenuation meaurement is about 30 dB for both beacons, increasing to 40 dB for B2 if the receivers are synchronized to B1. The accuracy is better than 0.5 dB. Cross-polarization discriminations of the order of 10 to 30 dB may be determined with an accuracy of 1 to 2 dB. A number of radiometers for complementary measurements of atmospheric attenuation of 13 to 30 GHz has also been constructed. A small multi-frequency system for operation around 22 GHz and 31 GHz is presently under development.
Bayesian variable order Markov models: Towards Bayesian predictive state representations
C. Dimitrakakis
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more st
Bayesian Analysis of Experimental Data
Directory of Open Access Journals (Sweden)
Lalmohan Bhar
2013-10-01
Full Text Available Analysis of experimental data from Bayesian point of view has been considered. Appropriate methodology has been developed for application into designed experiments. Normal-Gamma distribution has been considered for prior distribution. Developed methodology has been applied to real experimental data taken from long term fertilizer experiments.
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
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 remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
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 remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in...
ANALYSIS OF BAYESIAN CLASSIFIER ACCURACY
Directory of Open Access Journals (Sweden)
Felipe Schneider Costa
2013-01-01
Full Text Available The naÃ¯ve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naÃ¯ve assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables, the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naÃ¯ve Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naÃ¯ve Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
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)
Electronic warfare receivers and receiving systems
Poisel, Richard A
2014-01-01
Receivers systems are considered the core of electronic warfare (EW) intercept systems. Without them, the fundamental purpose of such systems is null and void. This book considers the major elements that make up receiver systems and the receivers that go in them.This resource provides system design engineers with techniques for design and development of EW receivers for modern modulations (spread spectrum) in addition to receivers for older, common modulation formats. Each major module in these receivers is considered in detail. Design information is included as well as performance tradeoffs o
Bayesian analysis of rare events
Straub, Daniel; 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.
Bayesian methods for measures of agreement
Broemeling, Lyle D
2009-01-01
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
Object oriented Bayesian networks have proven themselves useful in recent years. The idea of applying an object oriented approach to Bayesian networks has extended their scope to larger domains that can be divided into autonomous but interrelated entities. Object oriented Bayesian networks have...... 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...... by constructing a junction tree from this network. In this paper we propose a method for translating directly from object oriented Bayesian networks to junction trees, avoiding the intermediate translation. We pursue two main purposes: firstly, to maintain the original structure organized in an instance tree...
Bayesian Networks as a Decision Tool for O&M of Offshore Wind Turbines
DEFF Research Database (Denmark)
Nielsen, Jannie Jessen; Sørensen, John Dalsgaard
2010-01-01
Costs to operation and maintenance (O&M) of offshore wind turbines are large. This paper presents how influence diagrams can be used to assist in rational decision making for O&M. An influence diagram is a graphical representation of a decision tree based on Bayesian Networks. Bayesian Networks...... offer efficient Bayesian updating of a damage model when imperfect information from inspections/monitoring is available. The extension to an influence diagram offers the calculation of expected utilities for decision alternatives, and can be used to find the optimal strategy among different alternatives...
A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
Directory of Open Access Journals (Sweden)
Sho Fukuda
2014-12-01
Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks
Bayesian Variable Selection in Cost-Effectiveness Analysis
Directory of Open Access Journals (Sweden)
Miguel A. Negrín
2010-04-01
Full Text Available Linear regression models are often used to represent the cost and effectiveness of medical treatment. The covariates used may include sociodemographic variables, such as age, gender or race; clinical variables, such as initial health status, years of treatment or the existence of concomitant illnesses; and a binary variable indicating the treatment received. However, most studies estimate only one model, which usually includes all the covariates. This procedure ignores the question of uncertainty in model selection. In this paper, we examine four alternative Bayesian variable selection methods that have been proposed. In this analysis, we estimate the inclusion probability of each covariate in the real model conditional on the data. Variable selection can be useful for estimating incremental effectiveness and incremental cost, through Bayesian model averaging, as well as for subgroup analysis.
Bayesian phylogeny analysis via stochastic approximation Monte Carlo
Cheon, Sooyoung
2009-11-01
Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.
Flexible Bayesian Nonparametric Priors and Bayesian Computational Methods
Zhu, Weixuan
2016-01-01
The definition of vectors of dependent random probability measures is a topic of interest in Bayesian nonparametrics. They represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. Our first contribution is the introduction of novel multivariate vectors of two-parameter Poisson-Dirichlet process. The dependence is induced by applying a L´evy copula to the marginal L´evy intensities. Our attenti...
General and Local: Averaged k-Dependence Bayesian Classifiers
Directory of Open Access Journals (Sweden)
Limin Wang
2015-06-01
Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.
Bayesian inference of chemical kinetic models from proposed reactions
Galagali, Nikhil
2015-02-01
© 2014 Elsevier Ltd. Bayesian inference provides a natural framework for combining experimental data with prior knowledge to develop chemical kinetic models and quantify the associated uncertainties, not only in parameter values but also in model structure. Most existing applications of Bayesian model selection methods to chemical kinetics have been limited to comparisons among a small set of models, however. The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large. We present a new framework for tractable Bayesian model inference and uncertainty quantification using a large number of systematically generated model hypotheses. The approach involves imposing point-mass mixture priors over rate constants and exploring the resulting posterior distribution using an adaptive Markov chain Monte Carlo method. The posterior samples are used to identify plausible models, to quantify rate constant uncertainties, and to extract key diagnostic information about model structure-such as the reactions and operating pathways most strongly supported by the data. We provide numerical demonstrations of the proposed framework by inferring kinetic models for catalytic steam and dry reforming of methane using available experimental data.
Customizable Digital Receivers for Radar
Moller, Delwyn; Heavey, Brandon; Sadowy, Gregory
2008-01-01
Compact, highly customizable digital receivers are being developed for the system described in 'Radar Interferometer for Topographic Mapping of Glaciers and Ice Sheets' (NPO-43962), NASA Tech Briefs, Vol. 31, No. 7 (August 2007), page 72. The receivers are required to operate in unison, sampling radar returns received by the antenna elements in a digital beam-forming (DBF) mode. The design of these receivers could also be adapted to commercial radar systems. At the time of reporting the information for this article, there were no commercially available digital receivers capable of satisfying all of the operational requirements and compact enough to be mounted directly on the antenna elements. A provided figure depicts the overall system of which the digital receivers are parts. Each digital receiver includes an analog-to-digital converter (ADC), a demultiplexer (DMUX), and a field-programmable gate array (FPGA). The ADC effects 10-bit band-pass sampling of input signals having frequencies up to 3.5 GHz. The input samples are demultiplexed at a user-selectable rate of 1:2 or 1:4, then buffered in part of the FPGA that functions as a first-in/first-out (FIFO) memory. Another part of the FPGA serves as a controller for the ADC, DMUX, and FIFO memory and as an interface between (1) the rest of the receiver and (2) a front-panel data port (FPDP) bus, which is an industry-standard parallel data bus that has a high data-rate capability and multichannel configuration suitable for DBF. Still other parts of the FPGA in each receiver perform signal-processing functions. The digital receivers can be configured to operate in a stand-alone mode, or in a multichannel mode as needed for DBF. The customizability of the receiver makes it applicable to a broad range of system architectures. The capability for operation of receivers in either a stand-alone or a DBF mode enables the use of the receivers in an unprecedentedly wide variety of radar systems.
Bayesian approach to rough set
Marwala, Tshilidzi
2007-01-01
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
allows for a Bayesian formulation of the indicators whereby the experience and expertise of the inspection personnel may be fully utilized and consistently updated as frequentistic information is collected. The approach is illustrated on an example considering a concrete structure subject to corrosion......The problem of control quality of components is considered for the special case where the acceptable failure rate is low, the test costs are high and where it may be difficult or impossible to test the condition of interest directly. Based on the classical control theory and the concept of...... condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators. This...
BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS
Directory of Open Access Journals (Sweden)
Thordis Linda Thorarinsdottir
2011-05-01
Full Text Available 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 remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.
Bayesian Seismology of the Sun
Gruberbauer, Michael
2013-01-01
We perform a Bayesian grid-based analysis of the solar l=0,1,2 and 3 p modes obtained via BiSON in order to deliver the first Bayesian asteroseismic analysis of the solar composition problem. We do not find decisive evidence to prefer either of the contending chemical compositions, although the revised solar abundances (AGSS09) are more probable in general. We do find indications for systematic problems in standard stellar evolution models, unrelated to the consequences of inadequate modelling of the outer layers on the higher-order modes. The seismic observables are best fit by solar models that are several hundred million years older than the meteoritic age of the Sun. Similarly, meteoritic age calibrated models do not adequately reproduce the observed seismic observables. Our results suggest that these problems will affect any asteroseismic inference that relies on a calibration to the Sun.
Bayesian priors for transiting planets
Kipping, David M
2016-01-01
As astronomers push towards discovering ever-smaller transiting planets, it is increasingly common to deal with low signal-to-noise ratio (SNR) events, where the choice of priors plays an influential role in Bayesian inference. In the analysis of exoplanet data, the selection of priors is often treated as a nuisance, with observers typically defaulting to uninformative distributions. Such treatments miss a key strength of the Bayesian framework, especially in the low SNR regime, where even weak a priori information is valuable. When estimating the parameters of a low-SNR transit, two key pieces of information are known: (i) the planet has the correct geometric alignment to transit and (ii) the transit event exhibits sufficient signal-to-noise to have been detected. These represent two forms of observational bias. Accordingly, when fitting transits, the model parameter priors should not follow the intrinsic distributions of said terms, but rather those of both the intrinsic distributions and the observational ...
Bayesian Inference for Radio Observations
Lochner, Michelle; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
2015-01-01
(Abridged) New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inaccurate uncertainty estimates and biased results because such methods ignore any correlations between parameters. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realisation of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. Thi...
Bayesian inference on proportional elections.
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
A Bayesian Nonparametric IRT Model
Karabatsos, George
2015-01-01
This paper introduces a flexible Bayesian nonparametric Item Response Theory (IRT) model, which applies to dichotomous or polytomous item responses, and which can apply to either unidimensional or multidimensional scaling. This is an infinite-mixture IRT model, with person ability and item difficulty parameters, and with a random intercept parameter that is assigned a mixing distribution, with mixing weights a probit function of other person and item parameters. As a result of its flexibility...
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Mohammad-Djafari, Ali
2007-01-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian segmentation of hyperspectral images
Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali
2004-11-01
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
Bayesian Stable Isotope Mixing Models
Parnell, Andrew C.; Phillips, Donald L.; Bearhop, Stuart; Semmens, Brice X.; Ward, Eric J.; Moore, Jonathan W.; Andrew L Jackson; Inger, Richard
2012-01-01
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional m...
Bayesian Network--Response Regression
WANG, LU; Durante, Daniele; Dunson, David B.
2016-01-01
There is an increasing interest in learning how human brain networks vary with continuous traits (e.g., personality, cognitive abilities, neurological disorders), but flexible procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and Gaussian process priors to allow flexible shifts of the conditional expectation for a network-valued random variable across the feature space, while including subject-specific random eff...
Bayesian estimation of turbulent motion
Héas, P.; Herzet, C.; Mémin, E.; Heitz, D.; P. D. Mininni
2013-01-01
International audience Based on physical laws describing the multi-scale structure of turbulent flows, this article proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales. By reformulating this problem from a Bayesian perspective, an algorithm is proposed to jointly estimate motion, regularization hyper-parameters, and to select the ...
Elements of Bayesian experimental design
Energy Technology Data Exchange (ETDEWEB)
Sivia, D.S. [Rutherford Appleton Lab., Oxon (United Kingdom)
1997-09-01
We consider some elements of the Bayesian approach that are important for optimal experimental design. While the underlying principles used are very general, and are explained in detail in a recent tutorial text, they are applied here to the specific case of characterising the inferential value of different resolution peakshapes. This particular issue was considered earlier by Silver, Sivia and Pynn (1989, 1990a, 1990b), and the following presentation confirms and extends the conclusions of their analysis.
Skill Rating by Bayesian Inference
Di Fatta, Giuseppe; Haworth, Guy McCrossan; Regan, Kenneth W.
2009-01-01
Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of ...
Topics in Nonparametric Bayesian Statistics
2003-01-01
The intersection set of Bayesian and nonparametric statistics was almost empty until about 1973, but now seems to be growing at a healthy rate. This chapter gives an overview of various theoretical and applied research themes inside this field, partly complementing and extending recent reviews of Dey, Müller and Sinha (1998) and Walker, Damien, Laud and Smith (1999). The intention is not to be complete or exhaustive, but rather to touch on research areas of interest, partly by example.
Cover Tree Bayesian Reinforcement Learning
Tziortziotis, Nikolaos; Dimitrakakis, Christos; Blekas, Konstantinos
2013-01-01
This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration po...
Bayesian kinematic earthquake source models
Minson, S. E.; Simons, M.; Beck, J. L.; Genrich, J. F.; Galetzka, J. E.; Chowdhury, F.; Owen, S. E.; Webb, F.; Comte, D.; Glass, B.; Leiva, C.; Ortega, F. H.
2009-12-01
Most coseismic, postseismic, and interseismic slip models are based on highly regularized optimizations which yield one solution which satisfies the data given a particular set of regularizing constraints. This regularization hampers our ability to answer basic questions such as whether seismic and aseismic slip overlap or instead rupture separate portions of the fault zone. We present a Bayesian methodology for generating kinematic earthquake source models with a focus on large subduction zone earthquakes. Unlike classical optimization approaches, Bayesian techniques sample the ensemble of all acceptable models presented as an a posteriori probability density function (PDF), and thus we can explore the entire solution space to determine, for example, which model parameters are well determined and which are not, or what is the likelihood that two slip distributions overlap in space. Bayesian sampling also has the advantage that all a priori knowledge of the source process can be used to mold the a posteriori ensemble of models. Although very powerful, Bayesian methods have up to now been of limited use in geophysical modeling because they are only computationally feasible for problems with a small number of free parameters due to what is called the "curse of dimensionality." However, our methodology can successfully sample solution spaces of many hundreds of parameters, which is sufficient to produce finite fault kinematic earthquake models. Our algorithm is a modification of the tempered Markov chain Monte Carlo (tempered MCMC or TMCMC) method. In our algorithm, we sample a "tempered" a posteriori PDF using many MCMC simulations running in parallel and evolutionary computation in which models which fit the data poorly are preferentially eliminated in favor of models which better predict the data. We present results for both synthetic test problems as well as for the 2007 Mw 7.8 Tocopilla, Chile earthquake, the latter of which is constrained by InSAR, local high
Bayesian Kernel Mixtures for Counts
Canale, Antonio; David B Dunson
2011-01-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviatio...
Bayesian Optimization for Adaptive MCMC
Mahendran, Nimalan; Wang, Ziyu; Hamze, Firas; De Freitas, Nando
2011-01-01
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters o...
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;
2009-01-01
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 (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....
Quantile pyramids for Bayesian nonparametrics
2009-01-01
P\\'{o}lya trees fix partitions and use random probabilities in order to construct random probability measures. With quantile pyramids we instead fix probabilities and use random partitions. For nonparametric Bayesian inference we use a prior which supports piecewise linear quantile functions, based on the need to work with a finite set of partitions, yet we show that the limiting version of the prior exists. We also discuss and investigate an alternative model based on the so-called substitut...
Bayesian analysis of contingency tables
Gómez Villegas, Miguel A.; González Pérez, Beatriz
2005-01-01
The display of the data by means of contingency tables is used in different approaches to statistical inference, for example, to broach the test of homogeneity of independent multinomial distributions. We develop a Bayesian procedure to test simple null hypotheses versus bilateral alternatives in contingency tables. Given independent samples of two binomial distributions and taking a mixed prior distribution, we calculate the posterior probability that the proportion of successes in the first...
Bayesian Credit Ratings (new version)
Paola Cerchiello; Paolo Giudici
2013-01-01
In this contribution we aim at improving ordinal variable selection in the context of causal models. In this regard, we propose an approach that provides a formal inferential tool to compare the explanatory power of each covariate, and, therefore, to select an effective model for classification purposes. Our proposed model is Bayesian nonparametric, and, thus, keeps the amount of model specification to a minimum. We consider the case in which information from the covariates is at the ordinal ...
Target distribution in cooperative combat based on Bayesian optimization algorithm
Institute of Scientific and Technical Information of China (English)
Shi Zhifu; Zhang An; Wang Anli
2006-01-01
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.
Bayesian quantum frequency estimation in presence of collective dephasing
Macieszczak, Katarzyna; Fraas, Martin; Demkowicz-Dobrzanski, Rafal
2013-01-01
We advocate a Bayesian approach to optimal quantum frequency estimation - an important issue for future quantum enhanced atomic clock operation. The approach provides a clear insight into the interplay between decoherence and the extent of the prior knowledge in determining the optimal interrogation times and optimal estimation strategies. We propose a general framework capable of describing local oscillator noise as well as additional collective atomic dephasing effects. For a Gaussian noise...
Diffusion Estimation Of State-Space Models: Bayesian Formulation
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
Reims: IEEE, 2014. ISBN 978-1-4799-3693-9. [The 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014). Reims (FR), 21.09.2014-24.09.2014] R&D Projects: GA ČR(CZ) GP14-06678P Keywords : distributed estimation * state-space models * Bayesian estimation Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431804.pdf
Bayesian modeling and prediction of solar particles flux
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Kalová, J.
18/56/, 7/8 (2010), s. 228-230. ISSN 1210-7085 R&D Projects: GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : mathematical models * solar activity * solar flares * solar flux * solar particles Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/AS/dedecius-bayesian modeling and prediction of solar particles flux.pdf
Bayesian second law of thermodynamics
Bartolotta, Anthony; Carroll, Sean M.; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as Δ H (ρm,ρ ) + F |m≥0 , where Δ H (ρm,ρ ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρm and F |m is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
Quantum Inference on Bayesian Networks
Yoder, Theodore; Low, Guang Hao; Chuang, Isaac
2014-03-01
Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.
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...
Bayesian Posterior Distributions Without Markov Chains
Cole, Stephen R.; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B.
2012-01-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential ex...
Bayesian calibration of simultaneity in audiovisual temporal order judgments.
Directory of Open Access Journals (Sweden)
Shinya Yamamoto
Full Text Available After repeated exposures to two successive audiovisual stimuli presented in one frequent order, participants eventually perceive a pair separated by some lag time in the same order as occurring simultaneously (lag adaptation. In contrast, we previously found that perceptual changes occurred in the opposite direction in response to tactile stimuli, conforming to bayesian integration theory (bayesian calibration. We further showed, in theory, that the effect of bayesian calibration cannot be observed when the lag adaptation was fully operational. This led to the hypothesis that bayesian calibration affects judgments regarding the order of audiovisual stimuli, but that this effect is concealed behind the lag adaptation mechanism. In the present study, we showed that lag adaptation is pitch-insensitive using two sounds at 1046 and 1480 Hz. This enabled us to cancel lag adaptation by associating one pitch with sound-first stimuli and the other with light-first stimuli. When we presented each type of stimulus (high- or low-tone in a different block, the point of simultaneity shifted to "sound-first" for the pitch associated with sound-first stimuli, and to "light-first" for the pitch associated with light-first stimuli. These results are consistent with lag adaptation. In contrast, when we delivered each type of stimulus in a randomized order, the point of simultaneity shifted to "light-first" for the pitch associated with sound-first stimuli, and to "sound-first" for the pitch associated with light-first stimuli. The results clearly show that bayesian calibration is pitch-specific and is at work behind pitch-insensitive lag adaptation during temporal order judgment of audiovisual stimuli.
Bayesian networks with applications in reliability analysis
Langseth, Helge
2002-01-01
A common goal of the papers in this thesis is to propose, formalize and exemplify the use of Bayesian networks as a modelling tool in reliability analysis. The papers span work in which Bayesian networks are merely used as a modelling tool (Paper I), work where models are specially designed to utilize the inference algorithms of Bayesian networks (Paper II and Paper III), and work where the focus has been on extending the applicability of Bayesian networks to very large domains (Paper IV and ...
International Nuclear Information System (INIS)
In this paper, we present RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained
Bayesian phylogeography finds its roots.
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Rubens Angulo Filho
2002-01-01
Full Text Available Para avaliar a exatidão de posicionamento planimétrico do receptor GPS Trimble/Pro-XL, operando sob diferentes condições de cobertura vegetal (pastagem, seringueira, eucalipto e pinus, o equipamento foi posicionado alternadamente sobre 6 pontos, locados ao acaso nas áreas de estudo, variando o tempo de permanência (1 , 5 e 10 min mas com a mesma taxa de aquisição de dados (1 s fazendo-se, posteriormente, a correção diferencial (DGPS pós-processada dos dados. Os pontos também tiveram suas coordenadas levantadas pelo método topográfico, segundo a NBR 13133 - Execução de Levantamento Topográfico, para fins de comparação. De acordo com o método empregado e os resultados obtidos, foi possível separar as exatidões de posicionamento planimétrico, conforme o tipo de cobertura vegetal, em dois grupos: sem e com cobertura arbórea confirmando, assim, a interferência do dossel na recepção dos sinais emitidos pelos satélites GPS. O aumento do tempo de permanência melhorou a exatidão de posicionamento planimétrico, o que ratifica a escolha da metodologia de levantamento como sendo fundamental para a obtenção de bons resultados de posicionamento.To evaluate planimetric positioning accuracy of a GPS receiver (Trimble/Pro-XL, operating under different conditions of vegetation cover (pasture, rubber trees, eucalyptus and pine trees, 6 control points were located randomly in the study area. For comparison, their coordinates were first obtained by a conventional surveying method, according to NBR 13133 of Brazilian Surveying Standards. Afterwards, the GPS receiver was positioned on those control points, maintaining the acquisition rate of 1 s while changing the time for 1, 5 and 10 min, the DGPS method was used to correct the positioning coordinate data. According to the methodology applied and the results obtained, it was possible to distinguish planimetric positioning accuracy, according to the vegetation cover, in two groups
49 CFR 393.88 - Television receivers.
2010-10-01
... 49 Transportation 5 2010-10-01 2010-10-01 false Television receivers. 393.88 Section 393.88... NECESSARY FOR SAFE OPERATION Miscellaneous Parts and Accessories § 393.88 Television receivers. Any motor vehicle equipped with a television viewer, screen or other means of visually receiving a...
Towards Distributed Bayesian Estimation A Short Note on Selected Aspects
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
Prague: Institute of Information Theory and Automation, 2011, s. 67-72. ISBN 978-80-903834-6-3. [The 2nd International Workshop od Decision Making with Multiple Imperfect Decision Makers. Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011). Sierra Nevada (ES), 16.12.2011-16.12.2011] R&D Projects: GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : efficient estimation * a linear or nonlinear model * distributed estimation * Bayesian decision making Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/AS/dedecius-towards distributed bayesian estimation a short note on selected aspects.pdf
BaTMAn: Bayesian Technique for Multi-image Analysis
Casado, J; García-Benito, R; Guidi, G; Choudhury, O S; Bellocchi, E; Sánchez, S; Díaz, A I
2016-01-01
This paper describes the Bayesian Technique for Multi-image Analysis (BaTMAn), a novel image segmentation technique based on Bayesian statistics, whose main purpose is to characterize an astronomical dataset containing spatial information and perform a tessellation based on the measurements and errors provided as input. The algorithm will iteratively merge spatial elements as long as they are statistically consistent with carrying the same information (i.e. signal compatible with being identical within the errors). We illustrate its operation and performance with a set of test cases that comprises both synthetic and real Integral-Field Spectroscopic (IFS) data. Our results show that the segmentations obtained by BaTMAn adapt to the underlying structure of the data, regardless of the precise details of their morphology and the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in those regions where the signal is actually con...
Bayesian Query-Focused Summarization
Daumé, Hal
2009-01-01
We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of information in short queries. We show that approximate inference in BayeSum is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BayeSum can be understood as a justified query expansion technique in the language modeling for IR framework.
Numeracy, frequency, and Bayesian reasoning
Directory of Open Access Journals (Sweden)
Gretchen B. Chapman
2009-02-01
Full Text Available Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian credible interval construction for Poisson statistics
Institute of Scientific and Technical Information of China (English)
ZHU Yong-Sheng
2008-01-01
The construction of the Bayesian credible (confidence) interval for a Poisson observable including both the signal and background with and without systematic uncertainties is presented.Introducing the conditional probability satisfying the requirement of the background not larger than the observed events to construct the Bayesian credible interval is also discussed.A Fortran routine,BPOCI,has been developed to implement the calculation.
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…
Bayesian Statistics for Biological Data: Pedigree Analysis
Stanfield, William D.; Carlton, Matthew A.
2004-01-01
The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.
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…
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
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...... for complex networks can be derived and point out relevant literature....
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 and ...
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
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 by eva...
Bayesian analysis of exoplanet and binary orbits
Schulze-Hartung, Tim; Henning, Thomas
2012-01-01
We introduce BASE (Bayesian astrometric and spectroscopic exoplanet detection and characterisation tool), a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The capabilities of BASE are demonstrated using all publicly available data of the binary Mizar A.
Computational methods for Bayesian model choice
Robert, Christian P.; Wraith, Darren
2009-01-01
In this note, we shortly survey some recent approaches on the approximation of the Bayes factor used in Bayesian hypothesis testing and in Bayesian model choice. In particular, we reassess importance sampling, harmonic mean sampling, and nested sampling from a unified perspective.
Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
Huff, Daniel W.
Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time
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 BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data. PMID:26776199
Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations
Chen, Peng; Schwab, Christoph
2016-07-01
We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov-Galerkin high-fidelity ("HiFi") discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by the so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data assimilation
Directory of Open Access Journals (Sweden)
Dongsheng Chen
2016-01-01
Full Text Available Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values < 0.001 for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark and can reflect regional differences by using random parameters to improve the regional scale model accuracy.
Bazin, Eric; Dawson, Kevin J; Beaumont, Mark A
2010-06-01
ABC to Bayesian hierarchical models, and we apply it to detect microsatellite loci influenced by local selection. We demonstrate using receiver operating characteristic (ROC) analysis that this approach has comparable performance to a full-likelihood method and outperforms it when mutation rates are variable across loci. PMID:20382835
International Nuclear Information System (INIS)
MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm’s accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2 × 10−4), 283 for the intensity approach (p = 2 × 10−6) and 282 without
Solar dynamic heat receiver technology
Sedgwick, Leigh M.
1991-01-01
A full-size, solar dynamic heat receiver was designed to meet the requirements specified for electrical power modules on the U.S. Space Station, Freedom. The heat receiver supplies thermal energy to power a heat engine in a closed Brayton cycle using a mixture of helium-xenon gas as the working fluid. The electrical power output of the engine, 25 kW, requires a 100 kW thermal input throughout a 90 minute orbit, including when the spacecraft is eclipsed for up to 36 minutes from the sun. The heat receiver employs an integral thermal energy storage system utilizing the latent heat available through the phase change of a high-temperature salt mixture. A near eutectic mixture of lithium fluoride and calcium difluoride is used as the phase change material. The salt is contained within a felt metal matrix which enhances heat transfer and controls the salt void distribution during solidification. Fabrication of the receiver is complete and it was delivered to NASA for verification testing in a simulated low-Earth-orbit environment. This document reviews the receiver design and describes its fabrication history. The major elements required to operate the receiver during testing are also described.
Bayesian conformity assessment in presence of systematic measurement errors
Carobbi, Carlo; Pennecchi, Francesca
2016-04-01
Conformity assessment of the distribution of the values of a quantity is investigated by using a Bayesian approach. The effect of systematic, non-negligible measurement errors is taken into account. The analysis is general, in the sense that the probability distribution of the quantity can be of any kind, that is even different from the ubiquitous normal distribution, and the measurement model function, linking the measurand with the observable and non-observable influence quantities, can be non-linear. Further, any joint probability density function can be used to model the available knowledge about the systematic errors. It is demonstrated that the result of the Bayesian analysis here developed reduces to the standard result (obtained through a frequentistic approach) when the systematic measurement errors are negligible. A consolidated frequentistic extension of such standard result, aimed at including the effect of a systematic measurement error, is directly compared with the Bayesian result, whose superiority is demonstrated. Application of the results here obtained to the derivation of the operating characteristic curves used for sampling plans for inspection by variables is also introduced.
Bayesian networks in educational assessment
Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M
2015-01-01
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online. PMID:22523437
Hedging Strategies for Bayesian Optimization
Brochu, Eric; de Freitas, Nando
2010-01-01
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It is able to do this by sampling the objective using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We describe the method, which we call GP-Hedge, and show that this method almost always outperforms the best individual acquisition function.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Elvira, Clément; Dobigeon, Nicolas
2015-01-01
Sparse representations have proven their efficiency in solving a wide class of inverse problems encountered in signal and image processing. Conversely, enforcing the information to be spread uniformly over representation coefficients exhibits relevant properties in various applications such as digital communications. Anti-sparse regularization can be naturally expressed through an $\\ell_{\\infty}$-norm penalty. This paper derives a probabilistic formulation of such representations. A new probability distribution, referred to as the democratic prior, is first introduced. Its main properties as well as three random variate generators for this distribution are derived. Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding. Two Markov chain Monte Carlo (MCMC) algorithms are proposed to generate samples according to the posterior distribution. The first one is a standard Gibbs sampler. The seco...
State Information in Bayesian Games
Cuff, Paul
2009-01-01
Two-player zero-sum repeated games are well understood. Computing the value of such a game is straightforward. Additionally, if the payoffs are dependent on a random state of the game known to one, both, or neither of the players, the resulting value of the game has been analyzed under the framework of Bayesian games. This investigation considers the optimal performance in a game when a helper is transmitting state information to one of the players. Encoding information for an adversarial setting (game) requires a different result than rate-distortion theory provides. Game theory has accentuated the importance of randomization (mixed strategy), which does not find a significant role in most communication modems and source coding codecs. Higher rates of communication, used in the right way, allow the message to include the necessary random component useful in games.
Cooperative extensions of the Bayesian game
Ichiishi, Tatsuro
2006-01-01
This is the very first comprehensive monograph in a burgeoning, new research area - the theory of cooperative game with incomplete information with emphasis on the solution concept of Bayesian incentive compatible strong equilibrium that encompasses the concept of the Bayesian incentive compatible core. Built upon the concepts and techniques in the classical static cooperative game theory and in the non-cooperative Bayesian game theory, the theory constructs and analyzes in part the powerful n -person game-theoretical model characterized by coordinated strategy-choice with individualistic ince
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
Supra-Bayesian Combination of Probability Distributions
Czech Academy of Sciences Publication Activity Database
Sečkárová, Vladimíra
Veszprém : University of Pannonia, 2010, s. 112-117. ISBN 978-615-5044-00-7. [11th International PhD Workshop on Systems and Control. Veszprém (HU), 01.09.2010-03.09.2010] R&D Projects: GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : Supra-Bayesian approach * sharing of probabilistic information * Bayesian decision making Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2010/AS/seckarova-supra-bayesian combination of probability distributions.pdf
A Bayesian approach to matched field processing in uncertain ocean environments
Institute of Scientific and Technical Information of China (English)
LI Jianlong; PAN Xiang
2008-01-01
An approach of Bayesian Matched Field Processing(MFP)was discussed in the uncertain ocean environment.In this approach,uncertainty knowledge is modeled and spatial and temporal data Received by the array are fully used.Therefore,a mechanism for MFP is found.which well combines model-based and data-driven methods of uncertain field processing.By theoretical derivation,simulation analysis and the validation of the experimental array data at sea,we find that(1)the basic components of Bayesian matched field processors are the corresponding sets of Bartlett matched field processor,MVDR(minimum variance distortionless response)matched field processor,etc.;(2)Bayesian MVDR/Bartlett MFP are the weighted sum of the MVDR/Bartlett MFP,where the weighted coefficients are the values of the a posteriori probability;(3)with the uncertain ocean environment,Bayesian MFP can more correctly locate the source than MVDR MFP or Bartlett MFP;(4)Bayesian MFP call better suppress sidelobes of the ambiguity surfaces.
Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques
Saha, Bhaskar; Goebel, kai
2007-01-01
Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.
The Diagnosis of Reciprocating Machinery by Bayesian Networks
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A Bayesian Network is a reasoning tool based on probability theory and has many advantages that other reasoning tools do not have. This paper discusses the basic theory of Bayesian networks and studies the problems in constructing Bayesian networks. The paper also constructs a Bayesian diagnosis network of a reciprocating compressor. The example helps us to draw a conclusion that Bayesian diagnosis networks can diagnose reciprocating machinery effectively.
A Bayesian Networks in Intrusion Detection Systems
Directory of Open Access Journals (Sweden)
M. Mehdi
2007-01-01
Full Text Available Intrusion detection systems (IDSs have been widely used to overcome security threats in computer networks. Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behaviour which may result in a large number of false alarms caused by incorrect classification of events in current systems. We propose a new approach of an anomaly Intrusion detection system (IDS. It consists of building a reference behaviour model and the use of a Bayesian classification procedure associated to unsupervised learning algorithm to evaluate the deviation between current and reference behaviour. Continuous re-estimation of model parameters allows for real time operation. The use of recursive Log-likelihood and entropy estimation as a measure for monitoring model degradation related with behavior changes and the associated model update show that the accuracy of the event classification process is significantly improved using our proposed approach for reducing the missing-alarm.
Bayesian Model Selection for LISA Pathfinder
Karnesis, Nikolaos; Sopuerta, Carlos F; Gibert, Ferran; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Ferraioli, Luigi; Hewitson, Martin; Hueller, Mauro; Korsakova, Natalia; Plagnol, Eric; Vitale, and Stefano
2013-01-01
The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the LISA/eLISA concept. The Data Analysis (DA) team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF. These models are used for simulations, but more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the DA team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching to this problem is to recover the essential parameters of the LTP which describe the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes Factor between two competing models. In our analysis, we use three main different methods to estimate...
Process adjustment by a Bayesian approach
Directory of Open Access Journals (Sweden)
Daniel Duret
2015-12-01
Full Text Available In a production or measure situation, operators are required to make corrections to a process using the measurement of a sample. In both cases, it is always difficult to suggest a correction from a deviation. The correction is the result of two different deviations: one in set-up and the second in production. The latter is considered as noise. The objective of this paper is to propose an original approach to calculate the best correction using a Bayesian approach. A correction formula is given with three assumptions as regards adjusting the distribution: uniform, triangular and normal distribution. This paper gives a graphical interpretation of these different assumptions and a discussion of the results. Based on these results, the paper proposes a practical rule for calculating the most likely maladjustment in the case of a normal distribution. This practical rule gives the best adjustment using a simple relation (Adjustment = K*sample mean where K depends on the sample size, the ratio between the maladjustment and the short-term variability and a Type I risk of large maladjustment.
Optical communication receiver design
Alexander, Stephen B
2006-01-01
Copublished with IEE. This Tutorial Text provides an overview of design principles for receivers used in optical communication systems, intended for practicing engineers. The author reviews technologies used to construct optical links and illustrates the flow of system performance specifications into receiver requirements. Photodetector fundamentals, associated statistics, characteristics and performance issues are presented, together with a tutorial on noise analysis and the specific techniques needed to model optical receivers.
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++.
A Bayesian approach to model uncertainty
International Nuclear Information System (INIS)
A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given
An Intuitive Dashboard for Bayesian Network Inference
International Nuclear Information System (INIS)
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++
Bayesian Control for Concentrating Mixed Nuclear Waste
Welch, Robert L.; Smith, Clayton
2013-01-01
A control algorithm for batch processing of mixed waste is proposed based on conditional Gaussian Bayesian networks. The network is compiled during batch staging for real-time response to sensor input.
Delphi Accounts Receivable Module
Department of Transportation — Delphi accounts receivable module contains the following data elements, but are not limited to customer information, cash receipts, line of accounting details, bill...
Frayer, David T.; White, S.; Watts, G.; Stennes, M.; Maddalena, R. J.; Simon, R.; Pospieszalski, M.; Bryerton, E.
2013-01-01
The new 4mm receiver (67--93 GHz) for the Robert C. Byrd Green Bank Telescope (GBT) was built to take advantage of the improved surface accuracy of the dish. The low frequency end of the 3mm atmospheric window is not available with ALMA (National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Bayesian Variable Selection in Spatial Autoregressive Models
Jesus Crespo Cuaresma; Philipp Piribauer
2015-01-01
This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging tech...
Bayesian Analysis of Multivariate Probit Models
Siddhartha Chib; Edward Greenberg
1996-01-01
This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal dat...
Kernel Bayesian Inference with Posterior Regularization
Song, Yang; Jun ZHU; Ren, Yong
2016-01-01
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the posterior embedding estimator, which is faster and has comparable performance to the squared regularization in kernel Bayes' rule. This regularization coincides with a former th...
Fitness inheritance in the Bayesian optimization algorithm
Pelikan, Martin; Sastry, Kumara
2004-01-01
This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions...
Bayesian Network Models for Adaptive Testing
Czech Academy of Sciences Publication Activity Database
Plajner, Martin; Vomlel, Jiří
Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf
Nomograms for Visualization of Naive Bayesian Classifier
Možina, Martin; Demšar, Janez; Michael W Kattan; Zupan, Blaz
2004-01-01
Besides good predictive performance, the naive Bayesian classifier can also offer a valuable insight into the structure of the training data and effects of the attributes on the class probabilities. This structure may be effectively revealed through visualization of the classifier. We propose a new way to visualize the naive Bayesian model in the form of a nomogram. The advantages of the proposed method are simplicity of presentation, clear display of the effects of individual attribute value...
Subjective Bayesian Analysis: Principles and Practice
Goldstein, Michael
2006-01-01
We address the position of subjectivism within Bayesian statistics. We argue, first, that the subjectivist Bayes approach is the only feasible method for tackling many important practical problems. Second, we describe the essential role of the subjectivist approach in scientific analysis. Third, we consider possible modifications to the Bayesian approach from a subjectivist viewpoint. Finally, we address the issue of pragmatism in implementing the subjectivist approach.
An Entropy Search Portfolio for Bayesian Optimization
Shahriari, Bobak; Wang, Ziyu; Hoffman, Matthew W.; Bouchard-Côté, Alexandre; De Freitas, Nando
2014-01-01
Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i.e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance. While portfolio methods provide an effective, principled way of combining a collection of acquisition functions, they are often based on measures of past performance which can be misl...
A Bayesian Framework for Active Artificial Perception
Ferreira, Joao; Lobo, Jorge; Bessiere, Pierre; Castelo-Branco, M; Dias, Jorge
2012-01-01
In this text, we present a Bayesian framework for active multimodal perception of 3D structure and motion. The design of this framework finds its inspiration in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common egocentric spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach. In the process, we will contribute with efficient and robust probabilistic solutions for cycl...
Bayesian Classification in Medicine: The Transferability Question *
Zagoria, Ronald J.; Reggia, James A.; Price, Thomas R.; Banko, Maryann
1981-01-01
Using probabilities derived from a geographically distant patient population, we applied Bayesian classification to categorize stroke patients by etiology. Performance was assessed both by error rate and with a new linear accuracy coefficient. This approach to patient classification was found to be surprisingly accurate when compared to classification by two neurologists and to classification by the Bayesian method using “low cost” local and subjective probabilities. We conclude that for some...
Fuzzy Functional Dependencies and Bayesian Networks
Institute of Scientific and Technical Information of China (English)
LIU WeiYi(刘惟一); SONG Ning(宋宁)
2003-01-01
Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.
Evaluation System for a Bayesian Optimization Service
Dewancker, Ian; McCourt, Michael; Clark, Scott; Hayes, Patrick; Johnson, Alexandra; Ke, George
2016-01-01
Bayesian optimization is an elegant solution to the hyperparameter optimization problem in machine learning. Building a reliable and robust Bayesian optimization service requires careful testing methodology and sound statistical analysis. In this talk we will outline our development of an evaluation framework to rigorously test and measure the impact of changes to the SigOpt optimization service. We present an overview of our evaluation system and discuss how this framework empowers our resea...
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.
Bayesian Models of Brain and Behaviour
Penny, William
2012-01-01
This paper presents a review of Bayesian models of brain and behaviour. We first review the basic principles of Bayesian inference. This is followed by descriptions of sampling and variational methods for approximate inference, and forward and backward recursions in time for inference in dynamical models. The review of behavioural models covers work in visual processing, sensory integration, sensorimotor integration, and collective decision making. The review of brain models covers a range of...
Bayesian Approach to Handling Informative Sampling
Sikov, Anna
2015-01-01
In the case of informative sampling the sampling scheme explicitly or implicitly depends on the response variable. As a result, the sample distribution of response variable can- not be used for making inference about the population. In this research I investigate the problem of informative sampling from the Bayesian perspective. Application of the Bayesian approach permits solving the problems, which arise due to complexity of the models, being used for handling informative sampling. The main...
Sparsity in Bayesian Blind Source Separation and Deconvolution
Czech Academy of Sciences Publication Activity Database
Šmídl, Václav; Tichý, Ondřej
Berlin Heidelberg: Springer, 2013, s. 548-563. (Lecture Notes in Computer Science. vol. 8189. part II). ISBN 978-3-642-40990-5. ISSN 0302-9743. [The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013). Praha (CZ), 24.09.2013-26.09.2013] R&D Projects: GA ČR GA13-29225S Keywords : Blind Source Separation * Deconvolution * Sparsity * Scintigraphy Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/AS/tichy-sparsity in bayesian blind source separation and deconvolution.pdf
Development of bayesian update database for PRA data analysis (BUDDA)
International Nuclear Information System (INIS)
It is necessary what independent plant PRA (Probabilistic Risk Assessment) for risk informed applications of nuclear power plant. Therefore, it must build the environment that the utilities can efficiently collect PRA data, and can estimate PRA parameters without statistical expertise. This report explains development of failure events analysis DB for PRA failure rate computation using bayesian update technique. BUDDA has the function to compute failure rate with a combination of multiple DB (include the pre-installed data based on NUCIA), and to manage independent plant DB (failure events, number of components, operation time, number of demand , prior distributions). (author)
Bayesian demography 250 years after Bayes.
Bijak, Jakub; Bryant, John
2016-01-01
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. PMID:26902889
Inverse problems in the Bayesian framework
International Nuclear Information System (INIS)
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
Non-parametric Bayesian models of response function in dynamic image sequences
Czech Academy of Sciences Publication Activity Database
Tichý, Ondřej; Šmídl, Václav
-, - (2016). ISSN 1077-3142 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Response function * Blind source separation * Dynamic medical imaging * Probabilistic models * Bayesian methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.540, year: 2014 http://library.utia.cas.cz/separaty/2016/AS/tichy-0456983.pdf
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 Vision for Shape Recovery
Jalobeanu, Andre
2004-01-01
We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The observation process. also known as rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function. and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by Laplace approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.
Bayesian analysis of cosmic structures
Kitaura, Francisco-Shu
2011-01-01
We revise the Bayesian inference steps required to analyse the cosmological large-scale structure. Here we make special emphasis in the complications which arise due to the non-Gaussian character of the galaxy and matter distribution. In particular we investigate the advantages and limitations of the Poisson-lognormal model and discuss how to extend this work. With the lognormal prior using the Hamiltonian sampling technique and on scales of about 4 h^{-1} Mpc we find that the over-dense regions are excellent reconstructed, however, under-dense regions (void statistics) are quantitatively poorly recovered. Contrary to the maximum a posteriori (MAP) solution which was shown to over-estimate the density in the under-dense regions we obtain lower densities than in N-body simulations. This is due to the fact that the MAP solution is conservative whereas the full posterior yields samples which are consistent with the prior statistics. The lognormal prior is not able to capture the full non-linear regime at scales ...
BAYESIAN APPROACH OF DECISION PROBLEMS
Directory of Open Access Journals (Sweden)
DRAGOŞ STUPARU
2010-01-01
Full Text Available Management is nowadays a basic vector of economic development, a concept frequently used in our country as well as all over the world. Indifferently of the hierarchical level at which the managerial process is manifested, decision represents its essential moment, the supreme act of managerial activity. Its can be met in all fields of activity, practically having an unlimited degree of coverage, and in all the functions of management. It is common knowledge that the activity of any type of manger, no matter the hierarchical level he occupies, represents a chain of interdependent decisions, their aim being the elimination or limitation of the influence of disturbing factors that may endanger the achievement of predetermined objectives, and the quality of managerial decisions condition the progress and viability of any enterprise. Therefore, one of the principal characteristics of a successful manager is his ability to adopt the most optimal decisions of high quality. The quality of managerial decisions are conditioned by the manager’s general level of education and specialization, the manner in which they are preoccupied to assimilate the latest information and innovations in the domain of management’s theory and practice and the applying of modern managerial methods and techniques in the activity of management. We are presenting below the analysis of decision problems in hazardous conditions in terms of Bayesian theory – a theory that uses the probabilistic calculus.
Bayesian analysis of volcanic eruptions
Ho, Chih-Hsiang
1990-10-01
The simple Poisson model generally gives a good fit to many volcanoes for volcanic eruption forecasting. Nonetheless, empirical evidence suggests that volcanic activity in successive equal time-periods tends to be more variable than a simple Poisson with constant eruptive rate. An alternative model is therefore examined in which eruptive rate(λ) for a given volcano or cluster(s) of volcanoes is described by a gamma distribution (prior) rather than treated as a constant value as in the assumptions of a simple Poisson model. Bayesian analysis is performed to link two distributions together to give the aggregate behavior of the volcanic activity. When the Poisson process is expanded to accomodate a gamma mixing distribution on λ, a consequence of this mixed (or compound) Poisson model is that the frequency distribution of eruptions in any given time-period of equal length follows the negative binomial distribution (NBD). Applications of the proposed model and comparisons between the generalized model and simple Poisson model are discussed based on the historical eruptive count data of volcanoes Mauna Loa (Hawaii) and Etna (Italy). Several relevant facts lead to the conclusion that the generalized model is preferable for practical use both in space and time.
Das, Iswar; Stein, Alfred; Kerle, Norman; Dadhwal, Vinay K.
2012-12-01
Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular.
Comparison of Two Gas Selection Methodologies: An Application of Bayesian Model Averaging
Energy Technology Data Exchange (ETDEWEB)
Renholds, Andrea S.; Thompson, Sandra E.; Anderson, Kevin K.; Chilton, Lawrence K.
2006-03-31
One goal of hyperspectral imagery analysis is the detection and characterization of plumes. Characterization includes identifying the gases in the plumes, which is a model selection problem. Two gas selection methods compared in this report are Bayesian model averaging (BMA) and minimum Akaike information criterion (AIC) stepwise regression (SR). Simulated spectral data from a three-layer radiance transfer model were used to compare the two methods. Test gases were chosen to span the types of spectra observed, which exhibit peaks ranging from broad to sharp. The size and complexity of the search libraries were varied. Background materials were chosen to either replicate a remote area of eastern Washington or feature many common background materials. For many cases, BMA and SR performed the detection task comparably in terms of the receiver operating characteristic curves. For some gases, BMA performed better than SR when the size and complexity of the search library increased. This is encouraging because we expect improved BMA performance upon incorporation of prior information on background materials and gases.
Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer
Kim, Woojae; Kim, Ku Sang
2016-01-01
Objectives Breast cancer has a high rate of recurrence, resulting in the need for aggressive treatment and close follow-up. However, previously established classification guidelines, based on expert panels or regression models, are controversial. Prediction models based on machine learning show excellent performance, but they are not widely used because they cannot explain their decisions and cannot be presented on paper in the way that knowledge is customarily represented in the clinical world. The principal objective of this study was to develop a nomogram based on a naïve Bayesian model for the prediction of breast cancer recurrence within 5 years after breast cancer surgery. Methods The nomogram can provide a visual explanation of the predicted probabilities on a sheet of paper. We used a data set from a Korean tertiary teaching hospital of 679 patients who had undergone breast cancer surgery between 1994 and 2002. Seven prognostic factors were selected as independent variables for the model. Results The accuracy was 80%, and the area under the receiver operating characteristics curve (AUC) of the model was 0.81. Conclusions The nomogram can be easily used in daily practice to aid physicians and patients in making appropriate treatment decisions after breast cancer surgery. PMID:27200218
An assessment of Bayesian bias estimator for numerical weather prediction
Directory of Open Access Journals (Sweden)
J. Son
2008-12-01
Full Text Available Various statistical methods are used to process operational Numerical Weather Prediction (NWP products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets.
This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data.
By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.
Constitution and application of reactor make-up system's fault diagnostic Bayesian networks
International Nuclear Information System (INIS)
A fault diagnostic Bayesian network of reactor make-up system was constituted. The system's structure characters, operation rules and experts' experience were combined and an initial net was built. As the fault date sets were learned with the particle swarm optimization based Bayesian network structure, the structure of diagnostic net was completed and used to inference case. The built net can analyze diagnostic probability of every node in the net and afford assistant decision to fault diagnosis. (authors)
Modelling of Traffic Flow with Bayesian Autoregressive Model with Variable Partial Forgetting
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Nagy, Ivan; Hofman, Radek
Praha : ČVUT v Praze, 2011, s. 1-11. [CTU Workshop 2011. Praha (CZ), 01.02.2011-01.02.2011] Grant ostatní: ČVUT v Praze(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian modelling * traffic modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/AS/dedecius-modelling of traffic flow with bayesian autoregressive model with variable partial forgetting.pdf
An introduction to Gaussian Bayesian networks.
Grzegorczyk, Marco
2010-01-01
The extraction of regulatory networks and pathways from postgenomic data is important for drug -discovery and development, as the extracted pathways reveal how genes or proteins regulate each other. Following up on the seminal paper of Friedman et al. (J Comput Biol 7:601-620, 2000), Bayesian networks have been widely applied as a popular tool to this end in systems biology research. Their popularity stems from the tractability of the marginal likelihood of the network structure, which is a consistent scoring scheme in the Bayesian context. This score is based on an integration over the entire parameter space, for which highly expensive computational procedures have to be applied when using more complex -models based on differential equations; for example, see (Bioinformatics 24:833-839, 2008). This chapter gives an introduction to reverse engineering regulatory networks and pathways with Gaussian Bayesian networks, that is Bayesian networks with the probabilistic BGe scoring metric [see (Geiger and Heckerman 235-243, 1995)]. In the BGe model, the data are assumed to stem from a Gaussian distribution and a normal-Wishart prior is assigned to the unknown parameters. Gaussian Bayesian network methodology for analysing static observational, static interventional as well as dynamic (observational) time series data will be described in detail in this chapter. Finally, we apply these Bayesian network inference methods (1) to observational and interventional flow cytometry (protein) data from the well-known RAF pathway to evaluate the global network reconstruction accuracy of Bayesian network inference and (2) to dynamic gene expression time series data of nine circadian genes in Arabidopsis thaliana to reverse engineer the unknown regulatory network topology for this domain. PMID:20824469
Bayesian joint modeling of longitudinal and spatial survival AIDS data.
Martins, Rui; Silva, Giovani L; Andreozzi, Valeska
2016-08-30
Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for analyzing cancer and AIDS data. As both repeated measurements (longitudinal) and time-to-event (survival) outcomes are observed in an individual, a joint modeling is more appropriate because it takes into account the dependence between the two types of responses, which are often analyzed separately. We propose a Bayesian hierarchical model for jointly modeling longitudinal and survival data considering functional time and spatial frailty effects, respectively. That is, the proposed model deals with non-linear longitudinal effects and spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same region. This joint approach is applied to a cohort study of patients with HIV/AIDS in Brazil during the years 2002-2006. Our Bayesian joint model presents considerable improvements in the estimation of survival times of the Brazilian HIV/AIDS patients when compared with those obtained through a separate survival model and shows that the spatial risk of death is the same across the different Brazilian states. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26990773
Mobile sensor network noise reduction and recalibration using a Bayesian network
Xiang, Y.; Tang, Y.; Zhu, W.
2016-02-01
People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potential for atmospheric research. However, systems based on low-cost air quality sensors often suffer from sensor noise and drift. For the sensing systems to operate stably and reliably in real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors simultaneously. Our method improves upon the state-of-art Bayesian belief network techniques by incorporating the virtual evidence and adjusting the sensor calibration functions recursively.Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.
Robertson, C., Jr.
1984-01-01
Solar receiver uses ceramic honeycomb matrix to absorb heat from Sun and transfer it to working fluid at temperatures of 1,095 degrees and 1,650 degrees C. Drives gas turbine engine or provides heat for industrial processes.
Receiver Gain Modulation Circuit
Jones, Hollis; Racette, Paul; Walker, David; Gu, Dazhen
2011-01-01
A receiver gain modulation circuit (RGMC) was developed that modulates the power gain of the output of a radiometer receiver with a test signal. As the radiometer receiver switches between calibration noise references, the test signal is mixed with the calibrated noise and thus produces an ensemble set of measurements from which ensemble statistical analysis can be used to extract statistical information about the test signal. The RGMC is an enabling technology of the ensemble detector. As a key component for achieving ensemble detection and analysis, the RGMC has broad aeronautical and space applications. The RGMC can be used to test and develop new calibration algorithms, for example, to detect gain anomalies, and/or correct for slow drifts that affect climate-quality measurements over an accelerated time scale. A generalized approach to analyzing radiometer system designs yields a mathematical treatment of noise reference measurements in calibration algorithms. By treating the measurements from the different noise references as ensemble samples of the receiver state, i.e. receiver gain, a quantitative description of the non-stationary properties of the underlying receiver fluctuations can be derived. Excellent agreement has been obtained between model calculations and radiometric measurements. The mathematical formulation is equivalent to modulating the gain of a stable receiver with an externally generated signal and is the basis for ensemble detection and analysis (EDA). The concept of generating ensemble data sets using an ensemble detector is similar to the ensemble data sets generated as part of ensemble empirical mode decomposition (EEMD) with exception of a key distinguishing factor. EEMD adds noise to the signal under study whereas EDA mixes the signal with calibrated noise. It is mixing with calibrated noise that permits the measurement of temporal-functional variability of uncertainty in the underlying process. The RGMC permits the evaluation of EDA by
Bayesian calibration of power plant models for accurate performance prediction
International Nuclear Information System (INIS)
Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions
Institute of Scientific and Technical Information of China (English)
秦小琪; 李惊子; 王海燕
2001-01-01
Objective:To search for diagnostic critical value of nail creatinine (NCr) for acute renal failure (ARF) and chronic renal failure (CRF).Methods:Using receiver operating characteristic (ROC) curve method,we analyzed the diagnostic index for ARF and CRF——diagnostic critical value of NCr.Results:Because of individual distributing overlap of the NCr in ARF and CRF and because of the different determinate value for each selected cut-off point,the sensitivity (Se) and specificity (Sp) might vary,there would be differences in area under the ROC curve.The ROC curve area was 78.9 under the 5 cut-off point,and at peak point of the ROC curve the NCr was 84.9.Conclusions:Because ROC curve method combines Se and Sp to estimate the diagnostic critical value of disease index and determine the veracity by the area under the curve,this method has the practical value for clinical diagnosis when ROC curve area is 0.7-0.9 and NCr was 84.9.%目的：寻找指甲肌酐(NCr)鉴别急慢性肾衰竭的诊断界值。方法：应用ROC曲线的方法评价NCr作为鉴别诊断急慢性肾衰竭指标的界值及其优劣。结果：由于急慢性肾衰竭时NCr个体分布的重叠，选取各截断点下判定值的不同，其灵敏度、特异度发生变化，并使得ROC曲线下的面积不同。本研究所取5个截断点下曲线面积为78.9，NCr取值84.9处为曲线最凸点。结论：ROC曲线的面积在0.7～0.9之间有一定的准确性；以NCr测值84.9作为急慢性肾衰竭NCr的鉴别诊断界值有一定的临床价值。
Bayesball: A Bayesian hierarchical model for evaluating fielding in major league baseball
Jensen, Shane T.; Shirley, Kenneth E.; Wyner, Abraham J.
2008-01-01
The use of statistical modeling in baseball has received substantial attention recently in both the media and academic community. We focus on a relatively under-explored topic: the use of statistical models for the analysis of fielding based on high-resolution data consisting of on-field location of batted balls. We combine spatial modeling with a hierarchical Bayesian structure in order to evaluate the performance of individual fielders while sharing information between fielders at each posi...
Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks
Bingpeng Zhou; Qingchun Chen; Tiffany Jing Li; Pei Xiao
2014-01-01
The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offe...
Unsupervised Bayesian decomposition of multiunit EMG recordings using Tabu search.
Ge, Di; Le Carpentier, Eric; Farina, Dario
2010-03-01
Intramuscular electromyography (EMG) signals are usually decomposed with semiautomatic procedures that involve the interaction with an expert operator. In this paper, a Bayesian statistical model and a maximum a posteriori (MAP) estimator are used to solve the problem of multiunit EMG decomposition in a fully automatic way. The MAP estimation exploits both the likelihood of the reconstructed EMG signal and some physiological constraints, such as the discharge pattern regularity and the refractory period of muscle fibers, as prior information integrated in a Bayesian framework. A Tabu search is proposed to efficiently tackle the nondeterministic polynomial-time-hard problem of optimization w.r.t the motor unit discharge patterns. The method is fully automatic and was tested on simulated and experimental EMG signals. Compared with the semiautomatic decomposition performed by an expert operator, the proposed method resulted in an accuracy of 90.0% +/- 3.8% when decomposing single-channel intramuscular EMG signals recorded from the abductor digiti minimi muscle at contraction forces of 5% and 10% of the maximal force. The method can also be applied to the automatic identification and classification of spikes from other neural recordings. PMID:19457743
Dimensionality reduction in Bayesian estimation algorithms
Directory of Open Access Journals (Sweden)
G. W. Petty
2013-03-01
Full Text Available An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may utilize 9 to 13 channels to retrieve precipitation, for example. An objective method is described for distilling the relevant information content from N real channels into a much smaller number (M of pseudochannels while also regularizing the background (geophysical plus instrument noise component. The pseudochannels are linear combinations of the original N channels obtained via a two-stage principal component analysis of the dependent dataset. Bayesian retrievals based on a single pseudochannel applied to the independent dataset yield striking improvements in overall performance. The differences between the conventional Bayesian retrieval and reduced-dimensional Bayesian retrieval suggest that a major potential problem with conventional multichannel retrievals – whether Bayesian or not – lies in the common but often inappropriate assumption of diagonal error covariance. The dimensional reduction technique described herein avoids this problem by, in effect, recasting the retrieval problem in a coordinate system in which the desired covariance is lower-dimensional, diagonal, and unit magnitude.
Tactile length contraction as Bayesian inference.
Tong, Jonathan; Ngo, Vy; Goldreich, Daniel
2016-08-01
To perceive, the brain must interpret stimulus-evoked neural activity. This is challenging: The stochastic nature of the neural response renders its interpretation inherently uncertain. Perception would be optimized if the brain used Bayesian inference to interpret inputs in light of expectations derived from experience. Bayesian inference would improve perception on average but cause illusions when stimuli violate expectation. Intriguingly, tactile, auditory, and visual perception are all prone to length contraction illusions, characterized by the dramatic underestimation of the distance between punctate stimuli delivered in rapid succession; the origin of these illusions has been mysterious. We previously proposed that length contraction illusions occur because the brain interprets punctate stimulus sequences using Bayesian inference with a low-velocity expectation. A novel prediction of our Bayesian observer model is that length contraction should intensify if stimuli are made more difficult to localize. Here we report a tactile psychophysical study that tested this prediction. Twenty humans compared two distances on the forearm: a fixed reference distance defined by two taps with 1-s temporal separation and an adjustable comparison distance defined by two taps with temporal separation t ≤ 1 s. We observed significant length contraction: As t was decreased, participants perceived the two distances as equal only when the comparison distance was made progressively greater than the reference distance. Furthermore, the use of weaker taps significantly enhanced participants' length contraction. These findings confirm the model's predictions, supporting the view that the spatiotemporal percept is a best estimate resulting from a Bayesian inference process. PMID:27121574
A Large Sample Study of the Bayesian Bootstrap
Lo, Albert Y.
1987-01-01
An asymptotic justification of the Bayesian bootstrap is given. Large-sample Bayesian bootstrap probability intervals for the mean, the variance and bands for the distribution, the smoothed density and smoothed rate function are also provided.
Bayesian Methods for Radiation Detection and Dosimetry
Groer, Peter G
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...
BAMBI: blind accelerated multimodal Bayesian inference
Graff, Philip; Hobson, Michael P; Lasenby, Anthony
2011-01-01
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural network (NN) to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. We begin by demonstrating, with a few toy examples, the ability of a NN to learn complicated likelihood surfaces. BAMBI's ability to decrease running time for Bayesian inference is then demonstrated in the context of estimating cosmological parameters from WMAP and other observations. We show that valuable speed increases are achieved in addition to obtaining NNs trained on the likelihood functions for the different model and data combinations. These NNs can then be used for an...
Learning Bayesian Networks from Correlated Data
Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola
2016-05-01
Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Simpson, Edwin; Psorakis, Ioannis; Smith, Arfon
2012-01-01
Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed. Finally we present ...
Bayesian Image Reconstruction Based on Voronoi Diagrams
Cabrera, G F; Hitschfeld, N
2007-01-01
We present a Bayesian Voronoi image reconstruction technique (VIR) for interferometric data. Bayesian analysis applied to the inverse problem allows us to derive the a-posteriori probability of a novel parameterization of interferometric images. We use a variable Voronoi diagram as our model in place of the usual fixed pixel grid. A quantization of the intensity field allows us to calculate the likelihood function and a-priori probabilities. The Voronoi image is optimized including the number of polygons as free parameters. We apply our algorithm to deconvolve simulated interferometric data. Residuals, restored images and chi^2 values are used to compare our reconstructions with fixed grid models. VIR has the advantage of modeling the image with few parameters, obtaining a better image from a Bayesian point of view.
Bayesian Fusion of Multi-Band Images
Wei, Qi; Tourneret, Jean-Yves
2013-01-01
In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques. In particular, low spatial resolution hyperspectral and mult...
Internal dosimetry of uranium isotopes using bayesian inference methods
International Nuclear Information System (INIS)
A group of personnel at Los Alamos National Laboratory is routinely monitored for the presence of uranium isotopes by urine bioassay. Samples are analysed by alpha spectroscopy, and the results are examined for evidence of an intake of uranium. Because the measurement uncertainties are often comparable to the quantities of material we wish to detect, statistical considerations are crucial for the proper interpretation of the data. The problem is further complicated by the significant, but highly non-uniform, presence of uranium in local drinking water and, in some cases, food supply. Software originally developed for internal dosimetry of plutonium has been adapted to the problem of uranium dosimetry. The software uses an unfolding algorithm to calculate an approximate Bayesian solution to the problem of characterising any intakes which may have occurred, given the history of urine bioassay results for each individual in the monitored population. The program uses biokinetic models from ICRP Publications 68 and later, and a prior probability distribution derived empirically from the body of uranium bioassay data collected at Los Alamos over the operating history of the Laboratory. For each individual, the software creates a posterior probability distribution of intake quantity and solubility type as a function of time. From this distribution, estimates are made of the cumulative committed dose (CEDE) to each individual. Results of the method are compared with those obtained using an earlier classical (non-Bayesian) algorithm for uranium dosimetry. We also discuss the problem of distinguishing occupational intakes from intake of environmental uranium, within a Bayesian framework. (author)
Comparison of the Bayesian and Frequentist Approach to the Statistics
Hakala, Michal
2015-01-01
The Thesis deals with introduction to Bayesian statistics and comparing Bayesian approach with frequentist approach to statistics. Bayesian statistics is modern branch of statistics which provides an alternative comprehensive theory to the frequentist approach. Bayesian concepts provides solution for problems not being solvable by frequentist theory. In the thesis are compared definitions, concepts and quality of statistical inference. The main interest is focused on a point estimation, an in...
Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Kulis, Brian; Jordan, Michael I.
2011-01-01
Bayesian models offer great flexibility for clustering applications---Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such as k-means. In this paper, we revisit the k-means clustering algorithm from a Bayesian nonparametric viewpoint. Inspired by the asymptotic connection between k-means and mixtures...
Stennis group receives NESC award
2009-01-01
The NASA Engineering & Safety Center recently presented its Group Achievement Award to a Stennis team in recognition of technical excellence in evaluating the operational anomalies and reliability improvements associated with the space shuttle engine cut-off system. Stennis employees receiving the award were: (standing, l to r) Freddie Douglas (NASA), George Drouant (Jacobs Technology Inc.), Fred Abell (Jacobs), Robert Drackett (Jacobs) and Mike Smiles (NASA); (seated, l to r): Binh Nguyen (Jacobs), Stennis Director Gene Goldman and Joseph Lacker (NASA). Phillip Hebert of NASA is not pictured.
Solar thermal central receivers
International Nuclear Information System (INIS)
Market issues, environmental impact, and technology issues related to the Solar Central Receiver concept are addressed. The rationale for selection of the preferred configuration and working fluid are presented as the result of a joint utility-industry analysis. A $30 million conversion of Solar One to an external molten salt receiver would provide the intermediate step to a commercial demonstration plant. The first plant in this series could produce electricity at 11.2 cents/kWhr and the seventh at 8.2 cents/kWhr, completely competitive with projected costs of new utility plants in 1992
An Improved Algorithm of Bayesian Text Categorization
Directory of Open Access Journals (Sweden)
Tao Dong
2011-08-01
Full Text Available Text categorization is a fundamental methodology of text mining and a hot topic of the research of data mining and web mining in recent years. It plays an important role in building traditional information retrieval, web indexing architecture, Web information retrieval, and so on. This paper presents an improved algorithm of text categorization that combines the feature weighting technique with Naïve Bayesian classifier. Experimental results show that using the improved Gini index algorithm to feature weight can improve the performance of Naïve Bayesian classifier effectively. This algorithm obtains good application in the sensitive information recognition system.
Bayesian Optimisation Algorithm for Nurse Scheduling
Li, Jingpeng
2008-01-01
Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurses assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems.
Bayesian estimation and tracking a practical guide
Haug, Anton J
2012-01-01
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation
Bayesian Just-So Stories in Psychology and Neuroscience
Bowers, Jeffrey S.; Davis, Colin J.
2012-01-01
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak.…
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, t
A SAS Interface for Bayesian Analysis with WinBUGS
Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki
2008-01-01
Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…
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...
Flood alert system based on bayesian techniques
Gulliver, Z.; Herrero, J.; Viesca, C.; Polo, M. J.
2012-04-01
The problem of floods in the Mediterranean regions is closely linked to the occurrence of torrential storms in dry regions, where even the water supply relies on adequate water management. Like other Mediterranean basins in Southern Spain, the Guadalhorce River Basin is a medium sized watershed (3856 km2) where recurrent yearly floods occur , mainly in autumn and spring periods, driven by cold front phenomena. The torrential character of the precipitation in such small basins, with a concentration time of less than 12 hours, produces flash flood events with catastrophic effects over the city of Malaga (600000 inhabitants). From this fact arises the need for specific alert tools which can forecast these kinds of phenomena. Bayesian networks (BN) have been emerging in the last decade as a very useful and reliable computational tool for water resources and for the decision making process. The joint use of Artificial Neural Networks (ANN) and BN have served us to recognize and simulate the two different types of hydrological behaviour in the basin: natural and regulated. This led to the establishment of causal relationships between precipitation, discharge from upstream reservoirs, and water levels at a gauging station. It was seen that a recurrent ANN model working at an hourly scale, considering daily precipitation and the two previous hourly values of reservoir discharge and water level, could provide R2 values of 0.86. BN's results slightly improve this fit, but contribute with uncertainty to the prediction. In our current work to Design a Weather Warning Service based on Bayesian techniques the first steps were carried out through an analysis of the correlations between the water level and rainfall at certain representative points in the basin, along with the upstream reservoir discharge. The lower correlation found between precipitation and water level emphasizes the highly regulated condition of the stream. The autocorrelations of the variables were also
Pressure difference receiving ears
DEFF Research Database (Denmark)
Michelsen, Axel; Larsen, Ole Næsbye
2007-01-01
Directional sound receivers are useful for locating sound sources, and they can also partly compensate for the signal degradations caused by noise and reverberations. Ears may become inherently directional if sound can reach both surfaces of the eardrum. Attempts to understand the physics of such...
Institute of Scientific and Technical Information of China (English)
韩明
2009-01-01
This paper develops a new method,named E-Bayesian estimation method,to estimate the reliability parameters.The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product with Binomial distribution.Firstly,for the product reliability,the definitions of E-Bayesian estimation were given,and on the base,expressions of the E-Bayesian estimation and hierarchical Bayesian estimation of the products reliability was given.Secondly,discuss properties of the E-Bayesian estimation.Finally,the new method is applied to a real zero-failure data set,and as can be seen,it is both efficient and easy to operate.
Two-stage Bayesian models-application to ZEDB project
Energy Technology Data Exchange (ETDEWEB)
Bunea, C. [George Washington University, School of Applied Science, 1776 G Street, NW, Suite 108, Washington, DC 20052 (United States)]. E-mail: cornel@gwu.edu; Charitos, T. [Institute of Information and Computing Sciences, Padualaan 14, de Uithof, 3508 TB, Utrecht (Netherlands)]. E-mail: theodore@cs.uu.nl; Cooke, R.M. [Delft University of Technology, EWI Faculty, Mekelweg 4, 2628 CD, Delft (Netherlands)]. E-mail: r.m.cooke@ewi.tudelft.n1; Becker, G. [RISA, Krumme Str., Berlin 10627 (Germany)]. E-mail: guenter.becker@risa.de
2005-12-01
A well-known mathematical tool to analyze plant specific reliability data for nuclear power facilities is the two-stage Bayesian model. Such two-stage Bayesian models are standard practice nowadays, for example in the German ZEDB project or in the Swedish T-Book, although they may differ in their mathematical models and software implementation. In this paper, we review the mathematical model, its underlying assumptions and supporting arguments. Reasonable conditional assumptions are made to yield tractable and mathematically valid form for the failure rate at plant of interest, given failures and operational times at other plants in the population. The posterior probability of failure rate at plant of interest is sensitive to the choice of hyperprior parameters since the effect of hyperprior distribution will never be dominated by the effect of observation. The methods of Poern and Jeffrey for choosing distributions over hyperparameters are discussed. Furthermore, we will perform verification tasks associated with the theoretical model presented in this paper. The present software implementation produces good agreement with ZEDB results for various prior distributions. The difference between our results and those of ZEDB reflect differences that may arise from numerical implementation, as that would use different step size and truncation bounds.
Two-stage Bayesian models-application to ZEDB project
International Nuclear Information System (INIS)
A well-known mathematical tool to analyze plant specific reliability data for nuclear power facilities is the two-stage Bayesian model. Such two-stage Bayesian models are standard practice nowadays, for example in the German ZEDB project or in the Swedish T-Book, although they may differ in their mathematical models and software implementation. In this paper, we review the mathematical model, its underlying assumptions and supporting arguments. Reasonable conditional assumptions are made to yield tractable and mathematically valid form for the failure rate at plant of interest, given failures and operational times at other plants in the population. The posterior probability of failure rate at plant of interest is sensitive to the choice of hyperprior parameters since the effect of hyperprior distribution will never be dominated by the effect of observation. The methods of Poern and Jeffrey for choosing distributions over hyperparameters are discussed. Furthermore, we will perform verification tasks associated with the theoretical model presented in this paper. The present software implementation produces good agreement with ZEDB results for various prior distributions. The difference between our results and those of ZEDB reflect differences that may arise from numerical implementation, as that would use different step size and truncation bounds
Development of a Bayesian model to estimate health care outcomes in the severely wounded
Directory of Open Access Journals (Sweden)
Alexander Stojadinovic
2010-08-01
Full Text Available Alexander Stojadinovic1, John Eberhardt2, Trevor S Brown3, Jason S Hawksworth4, Frederick Gage3, Douglas K Tadaki3, Jonathan A Forsberg5, Thomas A Davis3, Benjamin K Potter5, James R Dunne6, E A Elster31Combat Wound Initiative Program, 4Department of Surgery, Walter Reed Army Medical Center, Washington, DC, USA; 2DecisionQ Corporation, Washington, DC, USA; 3Regenerative Medicine Department, Combat Casualty Care, Naval Medical Research Center, Silver Spring, MD, USA; 5Integrated Department of Orthopaedics and Rehabilitation, 6Department of Surgery, National Naval Medical Center, Bethesda, MD, USABackground: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations.Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN and step-wise logistic regression (LR. Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC from receiver operating characteristics (ROC curves.Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79, intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81, and wound healing (AUC: LR, 0.91; BBN, 0.72 showed strong AUC.Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold
Integer variables estimation problems: the Bayesian approach
Directory of Open Access Journals (Sweden)
G. Venuti
1997-06-01
Full Text Available In geodesy as well as in geophysics there are a number of examples where the unknown parameters are partly constrained to be integer numbers, while other parameters have a continuous range of possible values. In all such situations the ordinary least square principle, with integer variates fixed to the most probable integer value, can lead to paradoxical results, due to the strong non-linearity of the manifold of admissible values. On the contrary an overall estimation procedure assigning the posterior distribution to all variables, discrete and continuous, conditional to the observed quantities, like the so-called Bayesian approach, has the advantage of weighting correctly the possible errors in choosing different sets of integer values, thus providing a more realistic and stable estimate even of the continuous parameters. In this paper, after a short recall of the basics of Bayesian theory in section 2, we present the natural Bayesian solution to the problem of assessing the estimable signal from noisy observations in section 3 and the Bayesian solution to cycle slips detection and repair for a stream of GPS measurements in section 4. An elementary synthetic example is discussed in section 3 to illustrate the theory presented and more elaborate, though synthetic, examples are discussed in section 4 where realistic streams of GPS observations, with cycle slips, are simulated and then back processed.
Von Neumann was not a Quantum Bayesian.
Stacey, Blake C
2016-05-28
Wikipedia has claimed for over 3 years now that John von Neumann was the 'first quantum Bayesian'. In context, this reads as stating that von Neumann inaugurated QBism, the approach to quantum theory promoted by Fuchs, Mermin and Schack. This essay explores how such a claim is, historically speaking, unsupported. PMID:27091166
Von Neumann Was Not a Quantum Bayesian
Blake C. Stacey
2014-01-01
Wikipedia has claimed for over three years now that John von Neumann was the "first quantum Bayesian." In context, this reads as stating that von Neumann inaugurated QBism, the approach to quantum theory promoted by Fuchs, Mermin and Schack. This essay explores how such a claim is, historically speaking, unsupported.
A Bayesian Approach to Interactive Retrieval
Tague, Jean M.
1973-01-01
A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…
Bayesian Averaging is Well-Temperated
DEFF Research Database (Denmark)
Hansen, Lars Kai
2000-01-01
Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is l...
Perfect Bayesian equilibrium. Part II: epistemic foundations
Bonanno, Giacomo
2011-01-01
In a companion paper we introduced a general notion of perfect Bayesian equilibrium which can be applied to arbitrary extensive-form games. The essential ingredient of the proposed definition is the qualitative notion of AGM-consistency. In this paper we provide an epistemic foundation for AGM-consistency based on the AGM theory of belief revision.
Explanation mode for Bayesian automatic object recognition
Hazlett, Thomas L.; Cofer, Rufus H.; Brown, Harold K.
1992-09-01
One of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical object recognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian object recognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision--a very 'expert system' like capability. When various sources of incoming data are represented by C++ classes, it becomes possible to automatically backtrack the Bayesian data fusion process, assigning relative weights to the more significant datums and their combinations. A C++ object oriented engine is then able to synthesize 'English' like textural description of the Bayesian reasoning suitable for generalized presentation. Key concepts and examples are provided based on an actual object recognition problem.
Von Neumann Was Not a Quantum Bayesian
Stacey, Blake C
2014-01-01
Wikipedia has claimed for over two years now that John von Neumann was the "first quantum Bayesian." In context, this reads as stating that von Neumann inaugurated QBism, the approach to quantum theory promoted by Fuchs, Mermin and Schack. This essay explores how such a claim is, historically speaking, unsupported.
Scaling Bayesian network discovery through incremental recovery
Castelo, J.R.; Siebes, A.P.J.M.
1999-01-01
Bayesian networks are a type of graphical models that, e.g., allow one to analyze the interaction among the variables in a database. A well-known problem with the discovery of such models from a database is the ``problem of high-dimensionality''. That is, the discovery of a network from a database w
On Bayesian Nonparametric Continuous Time Series Models
Karabatsos, George; Walker, Stephen G.
2013-01-01
This paper is a note on the use of Bayesian nonparametric mixture models for continuous time series. We identify a key requirement for such models, and then establish that there is a single type of model which meets this requirement. As it turns out, the model is well known in multiple change-point problems.
Bayesian semiparametric dynamic Nelson-Siegel model
C. Cakmakli
2011-01-01
This paper proposes the Bayesian semiparametric dynamic Nelson-Siegel model where the density of the yield curve factors and thereby the density of the yields are estimated along with other model parameters. This is accomplished by modeling the error distributions of the factors according to a Diric
A Bayesian Bootstrap for a Finite Population
Lo, Albert Y.
1988-01-01
A Bayesian bootstrap for a finite population is introduced; its small-sample distributional properties are discussed and compared with those of the frequentist bootstrap for a finite population. It is also shown that the two are first-order asymptotically equivalent.
Bayesian analysis of Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2006-01-01
Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...
Bayesian calibration of car-following models
Van Hinsbergen, C.P.IJ.; Van Lint, H.W.C.; Hoogendoorn, S.P.; Van Zuylen, H.J.
2010-01-01
Recent research has revealed that there exist large inter-driver differences in car-following behavior such that different car-following models may apply to different drivers. This study applies Bayesian techniques to the calibration of car-following models, where prior distributions on each model p
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.
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world''s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas.
Bayesian Estimation of Thermonuclear Reaction Rates
Iliadis, Christian; Coc, Alain; Timmes, Frank; Starrfield, Sumner
2016-01-01
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 in the past to this problem, 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 extra-solar planets, gravitational waves, and type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present the first 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 d(p,$\\gamma$)$^3$He, $^3$He($^3$He,2p)$^4$He, and $^3$He($\\alpha$,$\\gamma$)$^7$Be reactions,...
An Approximate Bayesian Fundamental Frequency Estimator
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
Joint fundamental frequency and model order estimation is an important problem in several applications such as speech and music processing. In this paper, we develop an approximate estimation algorithm of these quantities using Bayesian inference. The inference about the fundamental frequency and...
Basics of Bayesian Learning - Basically Bayes
DEFF Research Database (Denmark)
Larsen, Jan
Tutorial presented at the IEEE Machine Learning for Signal Processing Workshop 2006, Maynooth, Ireland, September 8, 2006. The tutorial focuses on the basic elements of Bayesian learning and its relation to classical learning paradigms. This includes a critical discussion of the pros and cons. The...
Sensitivity to Sampling in Bayesian Word Learning
Xu, Fei; Tenenbaum, Joshua B.
2007-01-01
We report a new study testing our proposal that word learning may be best explained as an approximate form of Bayesian inference (Xu & Tenenbaum, in press). Children are capable of learning word meanings across a wide range of communicative contexts. In different contexts, learners may encounter different sampling processes generating the examples…
Janson, Thomas; Schindelhauer, Christian
2015-01-01
Pseudorandom PSK [1] enables parallel communication on the same carrier frequency and at the same time. We propose different signal processing methods to receive data modulated with pseudorandom PSK. This includes correlation with the carrier frequency which can be applied to signals in the kHz to MHz range and signal processing in the intermediate frequency where the correlation with the carrier frequency is performed analogous in the RF front end. We analyze the computation complexity for s...
Mustac, M.; Kim, S.; Tkalcic, H.; Rhie, J.; Chen, Y.; Ford, S. R.; Sebastian, N.
2015-12-01
Conventional approaches to inverse problems suffer from non-linearity and non-uniqueness in estimations of seismic structures and source properties. Estimated results and associated uncertainties are often biased by applied regularizations and additional constraints, which are commonly introduced to solve such problems. Bayesian methods, however, provide statistically meaningful estimations of models and their uncertainties constrained by data information. In addition, hierarchical and trans-dimensional (trans-D) techniques are inherently implemented in the Bayesian framework to account for involved error statistics and model parameterizations, and, in turn, allow more rigorous estimations of the same. Here, we apply Bayesian methods throughout the entire inference process to estimate seismic structures and source properties in Northeast Asia including east China, the Korean peninsula, and the Japanese islands. Ambient noise analysis is first performed to obtain a base three-dimensional (3-D) heterogeneity model using continuous broadband waveforms from more than 300 stations. As for the tomography of surface wave group and phase velocities in the 5-70 s band, we adopt a hierarchical and trans-D Bayesian inversion method using Voronoi partition. The 3-D heterogeneity model is further improved by joint inversions of teleseismic receiver functions and dispersion data using a newly developed high-efficiency Bayesian technique. The obtained model is subsequently used to prepare 3-D structural Green's functions for the source characterization. A hierarchical Bayesian method for point source inversion using regional complete waveform data is applied to selected events from the region. The seismic structure and source characteristics with rigorously estimated uncertainties from the novel Bayesian methods provide enhanced monitoring and discrimination of seismic events in northeast Asia.
A tutorial on Bayesian Normal linear regression
Klauenberg, Katy; Wübbeler, Gerd; Mickan, Bodo; Harris, Peter; Elster, Clemens
2015-12-01
Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view. Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach. These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.
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 clo...
A handbook for solar central receiver design
Energy Technology Data Exchange (ETDEWEB)
Falcone, P.K.
1986-12-01
This Handbook describes central receiver technology for solar thermal power plants. It contains a description and assessment of the major components in a central receiver system configured for utility scale production of electricity using Rankine-cycle steam turbines. It also describes procedures to size and optimize a plant and discussed examples from recent system analyses. Information concerning site selection criteria, cost estimation, construction, and operation and maintenance is also included, which should enable readers to perform design analyses for specific applications.
Approximation equation for EW receiver sensitivity
International Nuclear Information System (INIS)
A set of equations is presented for the calculation of the signal-to-noise ratio in decibels for required operational sensitivity of a microwave receiver. Given RF bandwidth and video bandwidth for an EW receiver, along with probability of detection and false alarm rate, S/N in decibels may be obtained. The method is less time-consuming than calculating sensitivity from curves, and the results obtained are within 1 dB of values obtained from curves. 8 references
Marcin, Martin; Abramovici, Alexander
2008-01-01
The software of a commercially available digital radio receiver has been modified to make the receiver function as a two-channel low-noise phase meter. This phase meter is a prototype in the continuing development of a phase meter for a system in which radiofrequency (RF) signals in the two channels would be outputs of a spaceborne heterodyne laser interferometer for detecting gravitational waves. The frequencies of the signals could include a common Doppler-shift component of as much as 15 MHz. The phase meter is required to measure the relative phases of the signals in the two channels at a sampling rate of 10 Hz at a root power spectral density digital receiver. The input RF signal is first fed to the input terminal of an analog-to-digital converter (ADC). To prevent aliasing errors in the ADC, the sampling rate must be at least twice the input signal frequency. The sampling rate of the ADC is governed by a sampling clock, which also drives a digital local oscillator (DLO), which is a direct digital frequency synthesizer. The DLO produces samples of sine and cosine signals at a programmed tuning frequency. The sine and cosine samples are mixed with (that is, multiplied by) the samples from the ADC, then low-pass filtered to obtain in-phase (I) and quadrature (Q) signal components. A digital signal processor (DSP) computes the ratio between the Q and I components, computes the phase of the RF signal (relative to that of the DLO signal) as the arctangent of this ratio, and then averages successive such phase values over a time interval specified by the user.
Adaptive antennas and receivers
Weiner, Melvin M
2005-01-01
In our modern age of remote sensing, wireless communication, and the nearly endless list of other antenna-based applications, complex problems require increasingly sophisticated solutions. Conventional antenna systems are no longer suited to high-noise or low-signal applications such as intrusion detection. Detailing highly effective approaches to non-Gaussian weak signal detection, Adaptive Antennas and Receivers provides an authoritative introduction to state-of-the-art research on the modeling, testing, and application of these technologies.Edited by innovative researcher and eminent expert
The Submillimeter Array Antennas and Receivers
Blundell, R.
The Submillimeter Array (SMA) was conceived at the Smithsonian Astrophysical Observatory in 1984 as a six element interferometer to operate in the major atmospheric windows from about 200 to 900 GHz. In 1996, the Academica Sinica Institute of Astronomy and Astrophysics of Taiwan joined the project and agreed to provide additional hardware to expand the interferometer to eight elements. All eight antennas are now operating at the observatory site on Mauna Kea, and astronomical observations have been made in the 230, 345, and 650 GHz bands. The SMA antennas have a diameter of 6 m, a surface accuracy of better than 25 micron rms, and can be reconfigured to provide spatial resolutions down to about 0.5" at 200 GHz and, eventually, 0.1" at 850 GHz. Coupling to the receiver package within each antenna is achieved via a beam waveguide, in a bent Nasmyth configuration, comprised of a flat tertiary mirror and two ellipsoidal mirrors that form a secondary pupil used for receiver calibration. An additional fixed mirror and a rotating wire grid polarizer are then used for receiver selection. Each antenna houses a single cryostat, with an integrated cryocooler capable of cooling up to eight receivers to 4 K. In the current configuration only three receiver bands are available: 175-255 GHz, 250-350 GHz, and 600-720 GHz, and simultaneous operation of the 650 GHz receiver with either of the lower frequency receivers is possible. Eventually dual polarization will be available from 325-350 GHz, and dual frequency operation will be possible, pairing either of the lower frequency receivers with any of the high frequency units: 325-425 GHz, 425-510 GHz, 600-720 GHz, and 800-900 GHz. Each receiver currently uses a single superconductor-insulator-superconductor junction as the mixing element, and has first stage intermediate frequency amplification at 4 K with an instantaneous bandwidth of 2.5 GHz, centered at 5 GHz. The mixers are of a fixed-tuned waveguide design, are inherently broad
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Everitt, Richard G
2012-01-01
Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian comput...
Bayesian evidence and predictivity of the inflationary paradigm
Gubitosi, Giulia; Lagos, Macarena; Magueijo, João; Allison, Rupert
2016-06-01
In this paper we consider the issue of paradigm evaluation by applying Bayes' theorem along the following nested hierarchy of progressively more complex structures: i) parameter estimation (within a model), ii) model selection and comparison (within a paradigm), iii) paradigm evaluation. In such a hierarchy the Bayesian evidence works both as the posterior's normalization at a given level and as the likelihood function at the next level up. Whilst raising no objections to the standard application of the procedure at the two lowest levels, we argue that it should receive a considerable modification when evaluating paradigms, when testability and fitting data are equally important. By considering toy models we illustrate how models and paradigms that are difficult to falsify are always favoured by the Bayes factor. We argue that the evidence for a paradigm should not only be high for a given dataset, but exceptional with respect to what it would have been, had the data been different. With this motivation we propose a measure which we term predictivity, as well as a prior to be incorporated into the Bayesian framework, penalising unpredictivity as much as not fitting data. We apply this measure to inflation seen as a whole, and to a scenario where a specific inflationary model is hypothetically deemed as the only one viable as a result of information alien to cosmology (e.g. Solar System gravity experiments, or particle physics input). We conclude that cosmic inflation is currently hard to falsify, but that this could change were external/additional information to cosmology to select one of its many models. We also compare this state of affairs to bimetric varying speed of light cosmology.
Bayesian network learning for natural hazard assessments
Vogel, Kristin
2016-04-01
Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables
Landslide hazards mapping using uncertain Naïve Bayesian classification method
Institute of Scientific and Technical Information of China (English)
毛伊敏; 张茂省; 王根龙; 孙萍萍
2015-01-01
Landslide hazard mapping is a fundamental tool for disaster management activities in Loess terrains. Aiming at major issues with these landslide hazard assessment methods based on Naïve Bayesian classification technique, which is difficult in quantifying those uncertain triggering factors, the main purpose of this work is to evaluate the predictive power of landslide spatial models based on uncertain Naïve Bayesian classification method in Baota district of Yan’an city in Shaanxi province, China. Firstly, thematic maps representing various factors that are related to landslide activity were generated. Secondly, by using field data and GIS techniques, a landslide hazard map was performed. To improve the accuracy of the resulting landslide hazard map, the strategies were designed, which quantified the uncertain triggering factor to design landslide spatial models based on uncertain Naïve Bayesian classification method named NBU algorithm. The accuracies of the area under relative operating characteristics curves (AUC) in NBU and Naïve Bayesian algorithm are 87.29%and 82.47%respectively. Thus, NBU algorithm can be used efficiently for landslide hazard analysis and might be widely used for the prediction of various spatial events based on uncertain classification technique.
Bayesian statistics for the calibration of the LISA Pathfinder experiment
Armano, M.; Audley, H.; Auger, G.; Binetruy, P.; Born, M.; Bortoluzzi, D.; Brandt, N.; Bursi, A.; Caleno, M.; Cavalleri, A.; Cesarini, A.; Cruise, M.; Danzmann, K.; Diepholz, I.; Dolesi, R.; Dunbar, N.; Ferraioli, L.; Ferroni, V.; Fitzsimons, E.; Freschi, M.; García Marirrodriga, C.; Gerndt, R.; Gesa, L.; Gibert, F.; Giardini, D.; Giusteri, R.; Grimani, C.; Harrison, I.; Heinzel, G.; Hewitson, M.; Hollington, D.; Hueller, M.; Huesler, J.; Inchauspé, H.; Jennrich, O.; Jetzer, P.; Johlander, B.; Karnesis, N.; Kaune, B.; Korsakova, N.; Killow, C.; Lloro, I.; Maarschalkerweerd, R.; Madden, S.; Mance, D.; Martin, V.; Martin-Porqueras, F.; Mateos, I.; McNamara, P.; Mendes, J.; Mitchell, E.; Moroni, A.; Nofrarias, M.; Paczkowski, S.; Perreur-Lloyd, M.; Pivato, P.; Plagnol, E.; Prat, P.; Ragnit, U.; Ramos-Castro, J.; Reiche, J.; Romera Perez, J. A.; Robertson, D.; Rozemeijer, H.; Russano, G.; Sarra, P.; Schleicher, A.; Slutsky, J.; Sopuerta, C. F.; Sumner, T.; Texier, D.; Thorpe, J.; Trenkel, C.; Tu, H. B.; Vitale, S.; Wanner, G.; Ward, H.; Waschke, S.; Wass, P.; Wealthy, D.; Wen, S.; Weber, W.; Wittchen, A.; Zanoni, C.; Ziegler, T.; Zweifel, P.
2015-05-01
The main goal of LISA Pathfinder (LPF) mission is to estimate the acceleration noise models of the overall LISA Technology Package (LTP) experiment on-board. This will be of crucial importance for the future space-based Gravitational-Wave (GW) detectors, like eLISA. Here, we present the Bayesian analysis framework to process the planned system identification experiments designed for that purpose. In particular, we focus on the analysis strategies to predict the accuracy of the parameters that describe the system in all degrees of freedom. The data sets were generated during the latest operational simulations organised by the data analysis team and this work is part of the LTPDA Matlab toolbox.
Fast Bayesian optimal experimental design for seismic source inversion
Long, Quan
2015-07-01
We develop a fast method for optimally designing experiments in the context of statistical seismic source inversion. In particular, we efficiently compute the optimal number and locations of the receivers or seismographs. The seismic source is modeled by a point moment tensor multiplied by a time-dependent function. The parameters include the source location, moment tensor components, and start time and frequency in the time function. The forward problem is modeled by elastodynamic wave equations. We show that the Hessian of the cost functional, which is usually defined as the square of the weighted L
Objectified quantification of uncertainties in Bayesian atmospheric inversions
Directory of Open Access Journals (Sweden)
A. Berchet
2014-07-01
Full Text Available Classical Bayesian atmospheric inversions process atmospheric observations and prior emissions, the two being connected by an observation operator picturing mainly the atmospheric transport. These inversions rely on prescribed errors in the observations, the prior emissions and the observation operator. At the meso-scale, inversion results are very sensitive to the prescribed error distributions, which are not accurately known. The classical Bayesian framework experiences difficulties in quantifying the impact of mis-specified error distributions on the optimized fluxes. In order to cope with this issue, we rely on recent research results and enhance the classical Bayesian inversion framework through a marginalization on all the plausible errors that can be prescribed in the system. The marginalization consists in computing inversions for all possible error distributions weighted by the probability of occurence of the error distributions. The posterior distribution of the fluxes calculated by the marginalization is complicated and not explicitly describable. We then carry out a Monte-Carlo sampling relying on an approximation of the probability of occurence of the error distributions. This approximation is deduced from the well-tested algorithm of the Maximum of Likelihood. Thus, the marginalized inversion relies on an automatic objectified diagnosis of the error statistics, without any prior knowledge about the matrices. It robustly includes the uncertainties on the error distributions, contrary to what is classically done with frozen expert-knowledge error statistics. Some expert knowledge is still used in the method for the choice of emission aggregation pattern and sampling protocol in order to reduce the computation costs of the method. The relevance and the robustness of the method is tested on a case study: the inversion of methane surface fluxes at the meso-scale with real observation sites in Eurasia. Observing System Simulation
Baker, Karl W. (Inventor); Dustin, Miles O. (Inventor)
1992-01-01
A plurality of heat pipes in a shell receive concentrated solar energy and transfer the energy to a heat activated system. To provide for even distribution of the energy despite uneven impingement of solar energy on the heat pipes, absence of solar energy at times, or failure of one or more of the heat pipes, energy storage means are disposed on the heat pipes which extend through a heat pipe thermal coupling means into the heat activated device. To enhance energy transfer to the heat activated device, the heat pipe coupling cavity means may be provided with extensions into the device. For use with a Stirling engine having passages for working gas, heat transfer members may be positioned to contact the gas and the heat pipes. The shell may be divided into sections by transverse walls. To prevent cavity working fluid from collecting in the extensions, a porous body is positioned in the cavity.
Bayesianism and inference to the best explanation
Directory of Open Access Journals (Sweden)
Valeriano IRANZO
2008-01-01
Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.
Learning Bayesian networks using genetic algorithm
Institute of Scientific and Technical Information of China (English)
Chen Fei; Wang Xiufeng; Rao Yimei
2007-01-01
A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not.Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.
A Bayesian Probabilistic Framework for Rain Detection
Directory of Open Access Journals (Sweden)
Chen Yao
2014-06-01
Full Text Available Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM, which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.
Machine learning a Bayesian and optimization perspective
Theodoridis, Sergios
2015-01-01
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...
Bayesian Peak Picking for NMR Spectra
Cheng, Yichen
2014-02-01
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Approximate Bayesian Computation: a nonparametric perspective
Blum, Michael
2010-01-01
Approximate Bayesian Computation is a family of likelihood-free inference techniques that are well-suited to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics s_obs from the data and simulating summary statistics for different values of the parameter theta. The posterior distribution is then approximated by an estimator of the conditional density g(theta|s_obs). In this paper, we derive the asymptotic bias and variance of the standard estimators of the posterior distribution which are based on rejection sampling and linear adjustment. Additionally, we introduce an original estimator of the posterior distribution based on quadratic adjustment and we show that its bias contains a fewer number of terms than the estimator with linear adjustment. Although we find that the estimators with adjustment are not universally superior to the estimator based on rejection sampling, we find that they can achieve better perfor...
Probabilistic forecasting and Bayesian data assimilation
Reich, Sebastian
2015-01-01
In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in ap...
Bayesian Magnetohydrodynamic Seismology of Coronal Loops
Arregui, Inigo
2011-01-01
We perform a Bayesian parameter inference in the context of resonantly damped transverse coronal loop oscillations. The forward problem is solved in terms of parametric results for kink waves in one-dimensional flux tubes in the thin tube and thin boundary approximations. For the inverse problem, we adopt a Bayesian approach to infer the most probable values of the relevant parameters, for given observed periods and damping times, and to extract their confidence levels. The posterior probability distribution functions are obtained by means of Markov Chain Monte Carlo simulations, incorporating observed uncertainties in a consistent manner. We find well localized solutions in the posterior probability distribution functions for two of the three parameters of interest, namely the Alfven travel time and the transverse inhomogeneity length-scale. The obtained estimates for the Alfven travel time are consistent with previous inversion results, but the method enables us to additionally constrain the transverse inho...
Bayesian parameter estimation for effective field theories
Wesolowski, S; Furnstahl, R J; Phillips, D R; Thapaliya, A
2015-01-01
We present procedures based on Bayesian statistics for effective field theory (EFT) parameter estimation from data. The extraction of low-energy constants (LECs) is guided by theoretical expectations that supplement such information in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools are developed that analyze the fit and ensure that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems and the extraction of LECs for the nucleon mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Bayesian image reconstruction: Application to emission tomography
Energy Technology Data Exchange (ETDEWEB)
Nunez, J.; Llacer, J.
1989-02-01
In this paper we propose a Maximum a Posteriori (MAP) method of image reconstruction in the Bayesian framework for the Poisson noise case. We use entropy to define the prior probability and likelihood to define the conditional probability. The method uses sharpness parameters which can be theoretically computed or adjusted, allowing us to obtain MAP reconstructions without the problem of the grey'' reconstructions associated with the pre Bayesian reconstructions. We have developed several ways to solve the reconstruction problem and propose a new iterative algorithm which is stable, maintains positivity and converges to feasible images faster than the Maximum Likelihood Estimate method. We have successfully applied the new method to the case of Emission Tomography, both with simulated and real data. 41 refs., 4 figs., 1 tab.
The Bayesian Who Knew Too Much
Benétreau-Dupin, Yann
2014-01-01
In several papers, John Norton has argued that Bayesianism cannot handle ignorance adequately due to its inability to distinguish between neutral and disconfirming evidence. He argued that this inability sows confusion in, e.g., anthropic reasoning in cosmology or the Doomsday argument, by allowing one to draw unwarranted conclusions from a lack of knowledge. Norton has suggested criteria for a candidate for representation of neutral support. Imprecise credences (families of credal probability functions) constitute a Bayesian-friendly framework that allows us to avoid inadequate neutral priors and better handle ignorance. The imprecise model generally agrees with Norton's representation of ignorance but requires that his criterion of self-duality be reformulated or abandoned
Social optimality in quantum Bayesian games
Iqbal, Azhar; Chappell, James M.; Abbott, Derek
2015-10-01
A significant aspect of the study of quantum strategies is the exploration of the game-theoretic solution concept of the Nash equilibrium in relation to the quantization of a game. Pareto optimality is a refinement on the set of Nash equilibria. A refinement on the set of Pareto optimal outcomes is known as social optimality in which the sum of players' payoffs is maximized. This paper analyzes social optimality in a Bayesian game that uses the setting of generalized Einstein-Podolsky-Rosen experiments for its physical implementation. We show that for the quantum Bayesian game a direct connection appears between the violation of Bell's inequality and the social optimal outcome of the game and that it attains a superior socially optimal outcome.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang;
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such...... adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... mechanism efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....
Bayesian parameter estimation for effective field theories
Wesolowski, S.; Klco, N.; Furnstahl, R. J.; Phillips, D. R.; Thapaliya, A.
2016-07-01
We present procedures based on Bayesian statistics for estimating, from data, the parameters of effective field theories (EFTs). The extraction of low-energy constants (LECs) is guided by theoretical expectations in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools is developed that analyzes the fit and ensures that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems, including the extraction of LECs for the nucleon-mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Applications of Bayesian spectrum representation in acoustics
Botts, Jonathan M.
This dissertation utilizes a Bayesian inference framework to enhance the solution of inverse problems where the forward model maps to acoustic spectra. A Bayesian solution to filter design inverts a acoustic spectra to pole-zero locations of a discrete-time filter model. Spatial sound field analysis with a spherical microphone array is a data analysis problem that requires inversion of spatio-temporal spectra to directions of arrival. As with many inverse problems, a probabilistic analysis results in richer solutions than can be achieved with ad-hoc methods. In the filter design problem, the Bayesian inversion results in globally optimal coefficient estimates as well as an estimate the most concise filter capable of representing the given spectrum, within a single framework. This approach is demonstrated on synthetic spectra, head-related transfer function spectra, and measured acoustic reflection spectra. The Bayesian model-based analysis of spatial room impulse responses is presented as an analogous problem with equally rich solution. The model selection mechanism provides an estimate of the number of arrivals, which is necessary to properly infer the directions of simultaneous arrivals. Although, spectrum inversion problems are fairly ubiquitous, the scope of this dissertation has been limited to these two and derivative problems. The Bayesian approach to filter design is demonstrated on an artificial spectrum to illustrate the model comparison mechanism and then on measured head-related transfer functions to show the potential range of application. Coupled with sampling methods, the Bayesian approach is shown to outperform least-squares filter design methods commonly used in commercial software, confirming the need for a global search of the parameter space. The resulting designs are shown to be comparable to those that result from global optimization methods, but the Bayesian approach has the added advantage of a filter length estimate within the same unified
Bayesian nonparametric regression with varying residual density
Pati, Debdeep; Dunson, David B.
2013-01-01
We consider the problem of robust Bayesian inference on the mean regression function allowing the residual density to change flexibly with predictors. The proposed class of models is based on a Gaussian process prior for the mean regression function and mixtures of Gaussians for the collection of residual densities indexed by predictors. Initially considering the homoscedastic case, we propose priors for the residual density based on probit stick-breaking (PSB) scale mixtures and symmetrized ...
Informed Source Separation: A Bayesian Tutorial
Knuth, Kevin
2013-01-01
Source separation problems are ubiquitous in the physical sciences; any situation where signals are superimposed calls for source separation to estimate the original signals. In this tutorial I will discuss the Bayesian approach to the source separation problem. This approach has a specific advantage in that it requires the designer to explicitly describe the signal model in addition to any other information or assumptions that go into the problem description. This leads naturally to the idea...
A Bayesian Modelling of Wildfires in Portugal
Silva, Giovani L.; Soares, Paulo; Marques, Susete; Dias, Inês M.; Oliveira, Manuela M.; Borges, Guilherme J.
2015-01-01
In the last decade wildfires became a serious problem in Portugal due to different issues such as climatic characteristics and nature of Portuguese forest. In order to analyse wildfire data, we employ beta regression for modelling the proportion of burned forest area, under a Bayesian perspective. Our main goal is to find out fire risk factors that influence the proportion of area burned and what may make a forest type susceptible or resistant to fire. Then, we analyse wildfire...
Market Segmentation Using Bayesian Model Based Clustering
Van Hattum, P.
2009-01-01
This dissertation deals with two basic problems in marketing, that are market segmentation, which is the grouping of persons who share common aspects, and market targeting, which is focusing your marketing efforts on one or more attractive market segments. For the grouping of persons who share common aspects a Bayesian model based clustering approach is proposed such that it can be applied to data sets that are specifically used for market segmentation. The cluster algorithm can handle very l...
Centralized Bayesian reliability modelling with sensor networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 19, č. 5 (2013), s. 471-482. ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant ostatní: GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf
Characteristic imsets for learning Bayesian network structure
Czech Academy of Sciences Publication Activity Database
Hemmecke, R.; Lindner, S.; Studený, Milan
2012-01-01
Roč. 53, č. 9 (2012), s. 1336-1349. ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf
Approximate Bayesian computation in population genetics.
Beaumont, Mark A; Zhang, Wenyang; Balding, David J.
2002-01-01
We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summ...
Nonparametric Bayesian Storyline Detection from Microtexts
Krishnan, Vinodh; Eisenstein, Jacob
2016-01-01
News events and social media are composed of evolving storylines, which capture public attention for a limited period of time. Identifying these storylines would enable many high-impact applications, such as tracking public interest and opinion in ongoing crisis events. However, this requires integrating temporal and linguistic information, and prior work takes a largely heuristic approach. We present a novel online non-parametric Bayesian framework for storyline detection, using the distance...
A Bayesian Concept Learning Approach to Crowdsourcing
DEFF Research Database (Denmark)
Viappiani, Paolo Renato; Zilles, Sandra; Hamilton, Howard J.;
2011-01-01
We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...... techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing...
Constrained bayesian inference of project performance models
Sunmola, Funlade
2013-01-01
Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in ...
Dual Control for Approximate Bayesian Reinforcement Learning
Klenske, Edgar D.; Hennig, Philipp
2015-01-01
Control of non-episodic, finite-horizon dynamical systems with uncertain dynamics poses a tough and elementary case of the exploration-exploitation trade-off. Bayesian reinforcement learning, reasoning about the effect of actions and future observations, offers a principled solution, but is intractable. We review, then extend an old approximate approach from control theory---where the problem is known as dual control---in the context of modern regression methods, specifically generalized line...
Bayesian biclustering of gene expression data
Liu Jun S; Gu Jiajun
2008-01-01
Abstract Background Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models. Results We developed a Bayesian biclustering model (BBC), and implemented a Gibbs sampling procedure for its statistical in...
A Theory of Bayesian Decision Making
Karni, Edi
2009-01-01
This paper presents a complete, choice-based, axiomatic Bayesian decision theory. It introduces a new choice set consisting of information-contingent plans for choosing actions and bets and subjective expected utility model with effect-dependent utility functions and action-dependent subjective probabilities which, in conjunction with the updating of the probabilities using Bayes' rule, gives rise to a unique prior and a set of action-dependent posterior probabilities representing the decisio...
A Bayesian framework for robotic programming
Lebeltel, Olivier; Diard, Julien; Bessiere, Pierre; Mazer, Emmanuel
2000-01-01
We propose an original method for programming robots based on Bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as well as the resolution methods. This formalism is inspired by the theory of probability, suggested by the physi...
Forming Object Concept Using Bayesian Network
Nakamura, Tomoaki; Nagai, Takayuki
2010-01-01
This chapter hase discussed a novel framework for object understanding. Implementation of the proposed framework using Bayesian Network has been presented. Although the result given in this paper is preliminary one, we have shown that the system can form object concept by observing the performance by human hands. The on-line learning is left for the future works. Moreover the model should be extended so that it can represent the object usage and work objects.
Approximate Bayesian inference for complex ecosystems
Michael P H Stumpf
2014-01-01
Mathematical models have been central to ecology for nearly a century. Simple models of population dynamics have allowed us to understand fundamental aspects underlying the dynamics and stability of ecological systems. What has remained a challenge, however, is to meaningfully interpret experimental or observational data in light of mathematical models. Here, we review recent developments, notably in the growing field of approximate Bayesian computation (ABC), that allow us to calibrate mathe...
Bayesian modeling and classification of neural signals
Lewicki, Michael S.
1994-01-01
Signal processing and classification algorithms often have limited applicability resulting from an inaccurate model of the signal's underlying structure. We present here an efficient, Bayesian algorithm for modeling a signal composed of the superposition of brief, Poisson-distributed functions. This methodology is applied to the specific problem of modeling and classifying extracellular neural waveforms which are composed of a superposition of an unknown number of action potentials CAPs). ...
Summary Statistics in Approximate Bayesian Computation
Prangle, Dennis
2015-01-01
This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) edited by S. Sisson, Y. Fan, and M. Beaumont. Since the earliest work on ABC, it has been recognised that using summary statistics is essential to produce useful inference results. This is because ABC suffers from a curse of dimensionality effect, whereby using high dimensional inputs causes large approximation errors in the output. It is therefore crucial to find low dimensional ...
Bayesian Semiparametric Modeling of Realized Covariance Matrices
Jin, Xin; John M Maheu
2014-01-01
This paper introduces several new Bayesian nonparametric models suitable for capturing the unknown conditional distribution of realized covariance (RCOV) matrices. Existing dynamic Wishart models are extended to countably infinite mixture models of Wishart and inverse-Wishart distributions. In addition to mixture models with constant weights we propose models with time-varying weights to capture time dependence in the unknown distribution. Each of our models can be combined with returns...
BEAST: Bayesian evolutionary analysis by sampling trees
Drummond Alexei J; Rambaut Andrew
2007-01-01
Abstract Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based m...
BEAST: Bayesian evolutionary analysis by sampling trees
Drummond, Alexei J.; Rambaut, Andrew
2007-01-01
Background: The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models su...
Benchmarking dynamic Bayesian network structure learning algorithms
Trabelsi, Ghada; Leray, Philippe; Ben Ayed, Mounir; Alimi, Adel
2012-01-01
Dynamic Bayesian Networks (DBNs) are probabilistic graphical models dedicated to modeling multivariate time series. Two-time slice BNs (2-TBNs) are the most current type of these models. Static BN structure learning is a well-studied domain. Many approaches have been proposed and the quality of these algorithms has been studied over a range of di erent standard networks and methods of evaluation. To the best of our knowledge, all studies about DBN structure learning use their own benchmarks a...
Bayesian Multi-Scale Optimistic Optimization
Wang, Ziyu; Shakibi, Babak; Jin, Lin; De Freitas, Nando
2014-01-01
Bayesian optimization is a powerful global optimization technique for expensive black-box functions. One of its shortcomings is that it requires auxiliary optimization of an acquisition function at each iteration. This auxiliary optimization can be costly and very hard to carry out in practice. Moreover, it creates serious theoretical concerns, as most of the convergence results assume that the exact optimum of the acquisition function can be found. In this paper, we introduce a new technique...
Bayesian mixture models for Poisson astronomical images
Guglielmetti, Fabrizia; Fischer, Rainer; Dose, Volker
2012-01-01
Astronomical images in the Poisson regime are typically characterized by a spatially varying cosmic background, large variety of source morphologies and intensities, data incompleteness, steep gradients in the data, and few photon counts per pixel. The Background-Source separation technique is developed with the aim to detect faint and extended sources in astronomical images characterized by Poisson statistics. The technique employs Bayesian mixture models to reliably detect the background as...
Complex Bayesian models: construction, and sampling strategies
Huston, Carolyn Marie
2011-01-01
Bayesian models are useful tools for realistically modeling processes occurring in the real world. In particular, we consider models for spatio-temporal data where the response vector is compositional, ie. has components that sum-to-one. A unique multivariate conditional hierarchical model (MVCAR) is proposed. Statistical methods for MVCAR models are well developed and we extend these tools for use with a discrete compositional response. We harness the advantages of an MVCAR model when the re...
The variational Bayes approximation in Bayesian filtering
Czech Academy of Sciences Publication Activity Database
Šmídl, Václav; Quinn, A.
Bryan : IEEE, 2006, s. 1-4. ISBN 1-4244-0469-X. [IEEE International Conference on Acoustics , Speech and Signal Processing. Toulouse (FR), 14.05.2006-19.05.2006] R&D Projects: GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : variational Bayes * Bayesian filtering Subject RIV: BD - Theory of Information
Towards Bayesian Deep Learning: A Survey
Wang, Hao; Yeung, Dit-Yan
2016-01-01
While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intel...
Improving Environmental Scanning Systems Using Bayesian Networks
Simon Welter; Jörg H. Mayer; Reiner Quick
2013-01-01
As companies’ environment is becoming increasingly volatile, scanning systems gain in importance. We propose a hybrid process model for such systems' information gathering and interpretation tasks that combines quantitative information derived from regression analyses and qualitative knowledge from expert interviews. For the latter, we apply Bayesian networks. We derive the need for such a hybrid process model from a literature review. We lay out our model to find a suitable set of business e...
On-line Bayesian System Identification
Romeres, Diego; Prando, Giulia; Pillonetto, Gianluigi; Chiuso, Alessandro
2016-01-01
We consider an on-line system identification setting, in which new data become available at given time steps. In order to meet real-time estimation requirements, we propose a tailored Bayesian system identification procedure, in which the hyper-parameters are still updated through Marginal Likelihood maximization, but after only one iteration of a suitable iterative optimization algorithm. Both gradient methods and the EM algorithm are considered for the Marginal Likelihood optimization. We c...
Dynamic Bayesian Networks for Cue Integration
Paul Maier; Frederike Petzschner
2012-01-01
If we want to understand how humans use contextual cues to solve tasks such as estimating distances from optic flow during path integration, our models need to represent the available information and formally describe how these representations are processed. In particular the temporal dynamics need to be incorporated, since it has been shown that humans exploit short-term experience gained in previous trials (Petzschner und Glasauer, 2011). Existing studies often use a Bayesian approach to mo...
The Bayesian Second Law of Thermodynamics
Bartolotta, Anthony; Carroll, Sean M.; Leichenauer, Stefan; Pollack, Jason
2015-01-01
We derive a generalization of the Second Law of Thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically-evolving system degrades over...
Bayesian analysis of matrix data with rstiefel
Hoff, Peter D.
2013-01-01
We illustrate the use of the R-package "rstiefel" for matrix-variate data analysis in the context of two examples. The first example considers estimation of a reduced-rank mean matrix in the presence of normally distributed noise. The second example considers the modeling of a social network of friendships among teenagers. Bayesian estimation for these models requires the ability to simulate from the matrix-variate von Mises-Fisher distributions and the matrix-variate Bingham distributions on...
An Explanation Mechanism for Bayesian Inferencing Systems
Norton, Steven W.
2013-01-01
Explanation facilities are a particularly important feature of expert system frameworks. It is an area in which traditional rule-based expert system frameworks have had mixed results. While explanations about control are well handled, facilities are needed for generating better explanations concerning knowledge base content. This paper approaches the explanation problem by examining the effect an event has on a variable of interest within a symmetric Bayesian inferencing system. We argue that...
Knowledge Engineering Within A Generalized Bayesian Framework
Barth, Stephen W.; Norton, Steven W.
2013-01-01
During the ongoing debate over the representation of uncertainty in Artificial Intelligence, Cheeseman, Lemmer, Pearl, and others have argued that probability theory, and in particular the Bayesian theory, should be used as the basis for the inference mechanisms of Expert Systems dealing with uncertainty. In order to pursue the issue in a practical setting, sophisticated tools for knowledge engineering are needed that allow flexible and understandable interaction with the underlying knowledge...
Towards Bayesian filtering on restricted support
Czech Academy of Sciences Publication Activity Database
Pavelková, Lenka; Kárný, Miroslav; Šmídl, Václav
Cambridge : University of Cambridge, 2006, s. 1-4. ISBN 978-1-4244-0579-4. [Nonlinear Statistical Siganl Processing Workshop 2006. Cambridge (GB), 13.09.2006-15.09.2006] R&D Projects: GA MŠk 1M0572; GA AV ČR 1ET100750401; GA MŠk 2C06001 Institutional research plan: CEZ:AV0Z10750506 Keywords : bayesian estimation * state model * restricted support Subject RIV: BC - Control Systems Theory
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326
Bayesian Spatial Modelling with R-INLA
Finn Lindgren; Håvard Rue
2015-01-01
The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized) linear mixed to spatial and spatio-temporal models. Combined with the stochastic...
Bayesian model for strategic level risk assessment in continuing airthworthiness of air transport
Jayakody-Arachchige, Dhanapala
2010-01-01
Continuing airworthiness (CAW) of aircraft is an essential pre-requisite for the safe operation of air transport. Human errors that occur in CAW organizations and processes could undermine the airworthiness and constitute a risk to flight safety. This thesis reports on a generic Bayesian model that has been designed to assess and quantify this risk. The model removes the vagueness inherent in the subjective methods of assessment of risk and its qualitative expression. Instead, relying on a...
Muhammad eZubair
2014-01-01
The investigation of the nuclear accidents reveals that the accumulation of various technical and nontechnical lapses compounded the nuclear disaster. By using Analytic Hierarchy Process (AHP) and Bayesian Network (BN) the present research signifies the technical and nontechnical issues of nuclear accidents. The study exposed that besides technical fixes such as enhanced engineering safety features and better siting choices, the critical ingredient for safe operation of nuclear reactors lie i...
Bayesian Analysis of Individual Level Personality Dynamics
Cripps, Edward; Wood, Robert E.; Beckmann, Nadin; Lau, John; Beckmann, Jens F.; Cripps, Sally Ann
2016-01-01
A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine whether the patterns of within-person responses on a 12-trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999). ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiraling. While Bayesian techniques have many potential advantages for the analyses of processes at the level of the individual, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques. PMID:27486415
Bayesian Analysis of Individual Level Personality Dynamics.
Cripps, Edward; Wood, Robert E; Beckmann, Nadin; Lau, John; Beckmann, Jens F; Cripps, Sally Ann
2016-01-01
A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine whether the patterns of within-person responses on a 12-trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999). ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiraling. While Bayesian techniques have many potential advantages for the analyses of processes at the level of the individual, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques. PMID:27486415
Particle identification in ALICE: a Bayesian approach
Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Shakeel; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Balasubramanian, Supraja; Baldisseri, Alberto; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Benacek, Pavel; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Bourjau, Christian; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Sanchez Gonzalez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Grachov, Oleg Anatolievich; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Gronefeld, Julius Maximilian; Grosse-Oetringhaus, Jan Fiete; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Harris, John William; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hillemanns, Hartmut; Hippolyte, Boris; Horak, David; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Inaba, Motoi; Incani, Elisa; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jahnke, Cristiane; Jakubowska, Monika Joanna; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karayan, Lilit; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Dong Jo; Kim, Daehyeok; Kim, Hyeonjoong; Kim, Jinsook; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kostarakis, Panagiotis; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kralik, Ivan; Kravcakova, Adela; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Ajay; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron De Guevara, Pedro; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Lutz, Tyler Harrison; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Davide; Pagano, Paola; Paic, Guy; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Scapparone, Eugenio; Scarlassara, Fernando; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shahzad, Muhammed Ikram; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Derradi De Souza, Rafael; Sozzi, Federica; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thakur, Dhananjaya; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trombetta, Giuseppe; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Vargas Trevino, Aurora Diozcora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Weiser, Dennis Franz; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yasin, Zafar; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Chunhui, Zhang; Zhang, Zuman; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym
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 PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss (dE/dx) and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high purity samples of identified particles in the decay channels ${\\rm K}_{\\rm S}^{\\rm 0}\\rightarrow \\pi^+\\pi^-$, $\\phi\\rightarrow {\\rm K}^-{\\rm K}^+$ and $\\Lambda\\rightarrow{\\rm p}\\pi^-$ in p–Pb collisions at $\\sqrt{s_{\\rm NN}}= 5.02$TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected $p_{\\rm T}$ spectra of pions, kaons, protons, and D$^0$ mesons in pp coll...
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
Bayesian Methods for Radiation Detection and Dosimetry
International Nuclear Information System (INIS)
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed compartmental activities. From the estimated probability densities of the model parameters we were able to derive the densities for compartmental activities for a two compartment catenary model at different times. We also calculated the average activities and their standard deviation for a simple two compartment model
Bayesian and Dempster–Shafer fusion
Indian Academy of Sciences (India)
Subhash Challa; Don Koks
2004-04-01
The Kalman Filter is traditionally viewed as a prediction–correction ﬁltering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman–Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster–Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster– Shafer theory and presenting the basic Dempster–Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.
Sparse Bayesian learning in ISAR tomography imaging
Institute of Scientific and Technical Information of China (English)
SU Wu-ge; WANG Hong-qiang; DENG Bin; WANG Rui-jun; QIN Yu-liang
2015-01-01
Inverse synthetic aperture radar (ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography (CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm (PFA) and the convolution back projection algorithm (CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing (CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of thel0-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed. Experimental results based on simulated and electromagnetic (EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.
Directory of Open Access Journals (Sweden)
Rasheda Arman Chowdhury
Full Text Available Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG or Magneto-EncephaloGraphy (MEG signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i brain activity may be modeled using cortical parcels and (ii brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM and the Hierarchical Bayesian (HB source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2 to 30 cm(2, whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.
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...... 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.......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...
Fuzzy Naive Bayesian for constructing regulated network with weights.
Zhou, Xi Y; Tian, Xue W; Lim, Joon S
2015-01-01
In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN. PMID:26405944
GNSS Software Receiver for UAVs
DEFF Research Database (Denmark)
Olesen, Daniel Madelung; Jakobsen, Jakob; von Benzon, Hans-Henrik;
2016-01-01
This paper describes the current activities of GPS/GNSS Software receiver development at DTU Space. GNSS Software receivers have received a great deal of attention in the last two decades and numerous implementations have already been presented. DTU Space has just recently started development of ...... our own GNSS software-receiver targeted for mini UAV applications, and we will in in this paper present our current progress and briefly discuss the benefits of Software Receivers in relation to our research interests....
Methodological problems of accounting received discounts
Shishkina Anna Vladimirovna
2015-01-01
The article contains the already existing approaches to business and financial accounting of the discounts, acquired by the discount-receiver, analyzes their advantages and disadvantages from the perspective of their correspondence with the active legislation and economic content of the discounts operations. The article also covers the problem of the absence of patterns for accounting discounting operations which would altogether pass the standard ФЗ [FZ] “On accounting”, Accounting Regulatio...
Being Bayesian in a quantum world
International Nuclear Information System (INIS)
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