Molecular Relaxation in Liquids
Bagchi, Biman
2012-01-01
This book brings together many different relaxation phenomena in liquids under a common umbrella and provides a unified view of apparently diverse phenomena. It aligns recent experimental results obtained with modern techniques with recent theoretical developments. Such close interaction between experiment and theory in this area goes back to the works of Einstein, Smoluchowski, Kramers' and de Gennes. Development of ultrafast laser spectroscopy recently allowed study of various relaxation processes directly in the time domain, with time scales going down to picosecond (ps) and femtosecond (fs
Relaxation time in disordered molecular systems
Energy Technology Data Exchange (ETDEWEB)
Rocha, Rodrigo P. [Departamento de Física, Universidade Federal de Santa Catarina, 88040-900 Florianópolis-SC (Brazil); Freire, José A., E-mail: jfreire@fisica.ufpr.br [Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba-PR (Brazil)
2015-05-28
Relaxation time is the typical time it takes for a closed physical system to attain thermal equilibrium. The equilibrium is brought about by the action of a thermal reservoir inducing changes in the system micro-states. The relaxation time is intuitively expected to increase with system disorder. We derive a simple analytical expression for this dependence in the context of electronic equilibration in an amorphous molecular system model. We find that the disorder dramatically enhances the relaxation time but does not affect its independence of the nature of the initial state.
Accelerating convergence of molecular dynamics-based structural relaxation
DEFF Research Database (Denmark)
Christensen, Asbjørn
2005-01-01
We describe strategies to accelerate the terminal stage of molecular dynamics (MD)based relaxation algorithms, where a large fraction of the computational resources are used. First, we analyze the qualitative and quantitative behavior of the QuickMin family of MD relaxation algorithms and explore...... the influence of spectral properties and dimensionality of the molecular system on the algorithm efficiency. We test two algorithms, the MinMax and Lanczos, for spectral estimation from an MD trajectory, and use this to derive a practical scheme of time step adaptation in MD relaxation algorithms to improve...
Vďačný, Peter
2015-08-01
The class Litostomatea comprises a diverse assemblage of free-living and endosymbiotic ciliates. To understand diversification dynamic of litostomateans, divergence times of their main groups were estimated with the Bayesian molecular dating, a technique allowing relaxation of molecular clock and incorporation of flexible calibration points. The class Litostomatea very likely emerged during the Cryogenian around 680 Mya. The origin of the subclass Rhynchostomatia is dated to about 415 Mya, while that of the subclass Haptoria to about 654 Mya. The order Pleurostomatida, emerging about 556 Mya, was recognized as the oldest group within the subclass Haptoria. The order Spathidiida appeared in the Paleozoic about 442 Mya. The three remaining haptorian orders evolved in the Paleozoic/Mesozoic periods: Didiniida about 419 Mya, Lacrymariida about 269 Mya, and Haptorida about 194 Mya. The subclass Trichostomatia originated from a spathidiid ancestor in the Mesozoic about 260 Mya. A further goal of this study was to investigate the impact of various settings on posterior divergence time estimates. The root placement and tree topology as well as the priors of the rate-drift model, birth-death process and nucleotide substitution rate, had no significant effect on calculation of posterior divergence time estimates. However, removal of calibration points could significantly change time estimates at some nodes.
Vibrational relaxation of guest and host in mixed molecular crystals
Hill, Jeffrey R.; Chronister, Eric L.; Chang, Ta-Chau; Kim, Hackjin; Postlewaite, Jay C.; Dlott, Dana D.
1988-02-01
Vibrational relaxation (VR) of dilute impurity molecules (naphthalene, anthracene) in crystalline host matrices (durene, naphthalene) is studied with the ps photon echo technique. The results obtained by echoes on vibrations in the electronically excited state are compared to previous ps time delayed coherent Raman studies of ground state vibrations of the pure host matrix. The relaxation channels for guest and host, and the effects of molecular and crystal structure on VR rates are determined.
Thermal relaxation of molecular oxygen in collisions with nitrogen atoms
Andrienko, Daniil A.; Boyd, Iain D.
2016-07-01
Investigation of O2-N collisions is performed by means of the quasi-classical trajectory method on the two lowest ab initio potential energy surfaces at temperatures relevant to hypersonic flows. A complete set of bound-bound and bound-free transition rates is obtained for each precollisional rovibrational state. Special attention is paid to the vibrational and rotational relaxations of oxygen as a result of chemically non-reactive interaction with nitrogen atoms. The vibrational relaxation of oxygen partially occurs via the formation of an intermediate NO2 complex. The efficient energy randomization results in rapid vibrational relaxation at low temperatures, compared to other molecular systems with a purely repulsive potential. The vibrational relaxation time, computed by means of master equation studies, is nearly an order of magnitude lower than the relaxation time in N2-O collisions. The rotational nonequilibrium starts to play a significant effect at translational temperatures above 8000 K. The present work provides convenient relations for the vibrational and rotational relaxation times as well as for the quasi-steady dissociation rate coefficient and thus fills a gap in data due to a lack of experimental measurements for this system.
Collective and molecular relaxation in ferroelectric liquid crystals
Wrobel, S.; Marzec, M.; Godlewska, Malgorzata; Gestblom, B.; Hiller, Steffen; Haase, Wolfgang
1995-08-01
Ferroelectric liquid crystals are molecular ferroelectrics showing up in the tilted liquid crystalline systems (SmC*, SmI*, SmF*) composed of chiral molecules. In this work, we present the dielectric, electro-optic, and calorimetric studies of a single component system: 3-octyloxy-6[2-fluor-4-(2-fluoroctyloxy)phenyl]-pyridine showing interesting ferroelectric properties. The compound exhibits a first order N*- SmC* phase transition which leads to a qualitatively new behavior, for instance the relaxation frequency of the soft mode below TC seems to be temperature independent. The high frequency relaxation process, connected with the reorientation around the long axis, is practically undisturbed at the N*-SmC* transition. Yet, it was found that in the SmC* phase, the best fit was obatined with two Cole-Cole functions yielding two relaxation times to describe a biased reorientation of molecules in the SmC* phase.
Zhu, Tianqi; Dos Reis, Mario; Yang, Ziheng
2015-03-01
Genetic sequence data provide information about the distances between species or branch lengths in a phylogeny, but not about the absolute divergence times or the evolutionary rates directly. Bayesian methods for dating species divergences estimate times and rates by assigning priors on them. In particular, the prior on times (node ages on the phylogeny) incorporates information in the fossil record to calibrate the molecular tree. Because times and rates are confounded, our posterior time estimates will not approach point values even if an infinite amount of sequence data are used in the analysis. In a previous study we developed a finite-sites theory to characterize the uncertainty in Bayesian divergence time estimation in analysis of large but finite sequence data sets under a strict molecular clock. As most modern clock dating analyses use more than one locus and are conducted under relaxed clock models, here we extend the theory to the case of relaxed clock analysis of data from multiple loci (site partitions). Uncertainty in posterior time estimates is partitioned into three sources: Sampling errors in the estimates of branch lengths in the tree for each locus due to limited sequence length, variation of substitution rates among lineages and among loci, and uncertainty in fossil calibrations. Using a simple but analogous estimation problem involving the multivariate normal distribution, we predict that as the number of loci ([Formula: see text]) goes to infinity, the variance in posterior time estimates decreases and approaches the infinite-data limit at the rate of 1/[Formula: see text], and the limit is independent of the number of sites in the sequence alignment. We then confirmed the predictions by using computer simulation on phylogenies of two or three species, and by analyzing a real genomic data set for six primate species. Our results suggest that with the fossil calibrations fixed, analyzing multiple loci or site partitions is the most effective way
Generalized extended Navier-Stokes theory: Multiscale spin relaxation in molecular fluids
DEFF Research Database (Denmark)
Hansen, Jesper Schmidt
2013-01-01
This paper studies the relaxation of the molecular spin angular velocity in the framework of generalized extended Navier-Stokes theory. Using molecular dynamics simulations, it is shown that for uncharged diatomic molecules the relaxation time decreases with increasing molecular moment of inertia...... per unit mass. In the regime of large moment of inertia the fast relaxation is wave-vector independent and dominated by the coupling between spin and the fluid streaming velocity, whereas for small inertia the relaxation is slow and spin diffusion plays a significant role. The fast wave...
Bayesian modelling of compositional heterogeneity in molecular phylogenetics.
Heaps, Sarah E; Nye, Tom M W; Boys, Richard J; Williams, Tom A; Embley, T Martin
2014-10-01
In molecular phylogenetics, standard models of sequence evolution generally assume that sequence composition remains constant over evolutionary time. However, this assumption is violated in many datasets which show substantial heterogeneity in sequence composition across taxa. We propose a model which allows compositional heterogeneity across branches, and formulate the model in a Bayesian framework. Specifically, the root and each branch of the tree is associated with its own composition vector whilst a global matrix of exchangeability parameters applies everywhere on the tree. We encourage borrowing of strength between branches by developing two possible priors for the composition vectors: one in which information can be exchanged equally amongst all branches of the tree and another in which more information is exchanged between neighbouring branches than between distant branches. We also propose a Markov chain Monte Carlo (MCMC) algorithm for posterior inference which uses data augmentation of substitutional histories to yield a simple complete data likelihood function that factorises over branches and allows Gibbs updates for most parameters. Standard phylogenetic models are not informative about the root position. Therefore a significant advantage of the proposed model is that it allows inference about rooted trees. The position of the root is fundamental to the biological interpretation of trees, both for polarising trait evolution and for establishing the order of divergence among lineages. Furthermore, unlike some other related models from the literature, inference in the model we propose can be carried out through a simple MCMC scheme which does not require problematic dimension-changing moves. We investigate the performance of the model and priors in analyses of two alignments for which there is strong biological opinion about the tree topology and root position.
Theoretical Studies of the Relaxation Matrix for Molecular Systems
Ma, Qiancheng; Boulet, C.
2016-06-01
The phenomenon of collisional transfer of intensity due to line mixing has an increasing importance for atmospheric monitoring. From a theoretical point of view, all relevant information about the collisional processes is contained in the relaxation matrix where the diagonal elements give half-widths and shifts, and the off-diagonal elements correspond to line interferences. For simple systems such as those consisting of diatom-atom or diatom-diatom, accurate fully quantum calculations based on interaction potentials are feasible. However, fully quantum calculations become unrealistic for more complex systems. On the other hand, the semi-classical Robert-Bonamy formalism, which has been widely used to calculate half-widths and shifts for decades, fails in calculating the off-diagonal matrix elements resulting from applying the isolated line approximation. As a result, in order to simulate atmospheric spectra where the effects from line mixing are important, semi-empirical fitting or scaling laws such as the energy corrected sudden (ECS) and the infinite order sudden (IOS) models are commonly used. Recently, we have found that in developing this semi-classical line shape theory, to rely on the isolated line approximation is not necessary. By eliminating this unjustified assumption, and accurately evaluating matrix elements of the exponential operators, we have developed a more capable formalism that enables one not only to reduce uncertainties for calculated half-widths and shifts, but also to calculate the whole relaxation matrix. This implies that we can address the line mixing with the semi-classical theory based on interaction potentials between molecular absorber and molecular perturber. We have applied this formalism for Raman and infrared spectra of linear and asymmetric-top molecules. Recently, the method has been extended into symmetric-tops with inverse symmetry such as the NH3 molecule. Our calculated half-widths of NH3 lines in the νb{1} and the pure
Vibrational relaxation and vibrational cooling in low temperature molecular crystals
Hill, Jeffrey R.; Chronister, Eric L.; Chang, Ta-Chau; Kim, Hackjin; Postlewaite, Jay C.; Dlott, Dana D.
1988-01-01
The processes of vibrational relaxation (VR) and vibrational cooling (VC) are investigated in low temperature crystals of complex molecules, specifically benzene, naphthalene, anthracene, and durene. In the VR process, a vibration is deexcited, while VC consists of many sequential and parallel VR steps which return the crystal to thermal equilibrium. A theoretical model is developed which relates the VR rate to the excess vibrational energy, the molecular structure, and the crystal structure. Specific relations are derived for the vibrational lifetime T1 in each of three regimes of excess vibrational energy. The regimes are the following: Low frequency regime I where VR occurs by emission of two phonons, intermediate frequency regime II where VR occurs by emission of one phonon and one vibration, and high frequency regime III where VR occurs by evolution into a dense bath of vibrational combinations. The VR rate in each regime depends on a particular multiphonon density of states and a few averaged anharmonic coefficients. The appropriate densities of states are calculated from spectroscopic data, and together with available VR data and new infrared and ps Raman data, the values of the anharmonic coefficients are determined for each material. The relationship between these parameters and the material properties is discussed. We then describe VC in a master equation formalism. The transition rate matrix for naphthalene is found using the empirically determined parameters of the above model, and the time dependent redistribution in each mode is calculated.
Iwaoka, Nobuyuki; Hagita, Katsumi; Takano, Hiroshi
2014-03-01
On the basis of relaxation mode analysis (RMA), we present an efficient method to estimate the linear viscoelasticity of polymer melts in a molecular dynamics (MD) simulation. Slow relaxation phenomena appeared in polymer melts cause a problem that a calculation of the stress relaxation function in MD simulations, especially in the terminal time region, requires large computational efforts. Relaxation mode analysis is a method that systematically extracts slow relaxation modes and rates of the polymer chain from the time correlation of its conformations. We show the computational cost may be drastically reduced by combining a direct calculation of the stress relaxation function based on the Green-Kubo formula with the relaxation rates spectra estimated by RMA. N. I. acknowledges the Graduate School Doctoral Student Aid Program from Keio University.
Molecular dynamics simulations of NMR relaxation and diffusion of bulk hydrocarbons and water
Singer, Philip M.; Asthagiri, Dilip; Chapman, Walter G.; Hirasaki, George J.
2017-04-01
Molecular dynamics (MD) simulations are used to investigate 1H nuclear magnetic resonance (NMR) relaxation and diffusion of bulk n-C5H12 to n-C17H36 hydrocarbons and bulk water. The MD simulations of the 1H NMR relaxation times T1,2 in the fast motion regime where T1 =T2 agree with measured (de-oxygenated) T2 data at ambient conditions, without any adjustable parameters in the interpretation of the simulation data. Likewise, the translational diffusion DT coefficients calculated using simulation configurations agree with measured diffusion data at ambient conditions. The agreement between the predicted and experimentally measured NMR relaxation times and diffusion coefficient also validate the forcefields used in the simulation. The molecular simulations naturally separate intramolecular from intermolecular dipole-dipole interactions helping bring new insight into the two NMR relaxation mechanisms as a function of molecular chain-length (i.e. carbon number). Comparison of the MD simulation results of the two relaxation mechanisms with traditional hard-sphere models used in interpreting NMR data reveals important limitations in the latter. With increasing chain length, there is substantial deviation in the molecular size inferred on the basis of the radius of gyration from simulation and the fitted hard-sphere radii required to rationalize the relaxation times. This deviation is characteristic of the local nature of the NMR measurement, one that is well-captured by molecular simulations.
Angelikopoulos, Panagiotis; Papadimitriou, Costas; Koumoutsakos, Petros
2012-10-01
We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore, adaptive surrogate models are proposed in order to reduce the computational cost associated with the large number of MD model runs. The effectiveness and computational efficiency of the proposed Bayesian framework is demonstrated in MD simulations of liquid and gaseous argon.
Bayesian molecular clock dating of species divergences in the genomics era.
dos Reis, Mario; Donoghue, Philip C J; Yang, Ziheng
2016-02-01
Five decades have passed since the proposal of the molecular clock hypothesis, which states that the rate of evolution at the molecular level is constant through time and among species. This hypothesis has become a powerful tool in evolutionary biology, making it possible to use molecular sequences to estimate the geological ages of species divergence events. With recent advances in Bayesian clock dating methodology and the explosive accumulation of genetic sequence data, molecular clock dating has found widespread applications, from tracking virus pandemics and studying the macroevolutionary process of speciation and extinction to estimating a timescale for life on Earth.
Directory of Open Access Journals (Sweden)
Vizcaíno Sergio F
2004-04-01
Full Text Available Abstract Background Comparative genomic data among organisms allow the reconstruction of their phylogenies and evolutionary time scales. Molecular timings have been recently used to suggest that environmental global change have shaped the evolutionary history of diverse terrestrial organisms. Living xenarthrans (armadillos, anteaters and sloths constitute an ideal model for studying the influence of past environmental changes on species diversification. Indeed, extant xenarthran species are relicts from an evolutionary radiation enhanced by their isolation in South America during the Tertiary era, a period for which major climate variations and tectonic events are relatively well documented. Results We applied a Bayesian approach to three nuclear genes in order to relax the molecular clock assumption while accounting for differences in evolutionary dynamics among genes and incorporating paleontological uncertainties. We obtained a molecular time scale for the evolution of extant xenarthrans and other placental mammals. Divergence time estimates provide substantial evidence for contemporaneous diversification events among independent xenarthran lineages. This correlated pattern of diversification might possibly relate to major environmental changes that occurred in South America during the Cenozoic. Conclusions The observed synchronicity between planetary and biological events suggests that global change played a crucial role in shaping the evolutionary history of extant xenarthrans. Our findings open ways to test this hypothesis further in other South American mammalian endemics like hystricognath rodents, platyrrhine primates, and didelphid marsupials.
Molecular motions in thermotropic liquid crystals studied by NMR spin-lattice relaxation
Energy Technology Data Exchange (ETDEWEB)
Zamar, R.C.; Gonzalez, C.E.; Mensio, O. [Cordoba Univ. Nacional (Argentina). Facultad de Matematica, Astronomia y Fisica
1998-12-01
Nuclear magnetic resonance relaxation experiments with field cycling techniques proved to be a valuable tool for studying molecular motions in liquid crystals, allowing a very broad Larmor frequency variation, sufficient to separate the cooperative motions from the liquid like molecular diffusion. In new experiments combining NMR field cycling with the Jeener-Broekaert order-transfer pulse sequence, it is possible to measure the dipolar order relaxation time (T{sub 1D}), in addition to the conventional Zeeman relaxation time (T{sub 1Z}) in a frequency range of several decades. When applying this technique to nematic thermotropic liquid crystals, T{sub 1D} showed to depend almost exclusively on the order fluctuation of the director mechanism in the whole frequency range. This unique characteristic of T{sub 1D} makes dipolar order relaxation experiments specially useful for studying the frequency and temperature dependence of the spectral properties of the collective motions. (author)
Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger
2017-01-01
Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods. PMID:28166542
Molecular excitation dynamics and relaxation quantum theory and spectroscopy
Valkunas, Leonas; Mancal, Tomas
2013-01-01
Meeting the need for a work that brings together quantum theory and spectroscopy to convey excitation processes to advanced students and specialists wishing to conduct research and understand the entire field rather than just single aspects.Written by an experienced author and recognized authority in the field, this text covers numerous applications and offers examples taken from different disciplines. As a result, spectroscopists, molecular physicists, physical chemists, and biophysicists will all find this a must-have for their research. Also suitable as supplementary reading in graduate
A study of internal energy relaxation in shocks using molecular dynamics based models
Energy Technology Data Exchange (ETDEWEB)
Li, Zheng, E-mail: zul107@psu.edu; Parsons, Neal, E-mail: neal.parsons@cd-adapco.com [Department of Aerospace Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802 (United States); Levin, Deborah A., E-mail: deblevin@illinois.edu [Department of Aerospace Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-2935 (United States)
2015-10-14
Recent potential energy surfaces (PESs) for the N{sub 2} + N and N{sub 2} + N{sub 2} systems are used in molecular dynamics (MD) to simulate rates of vibrational and rotational relaxations for conditions that occur in hypersonic flows. For both chemical systems, it is found that the rotational relaxation number increases with the translational temperature and decreases as the rotational temperature approaches the translational temperature. The vibrational relaxation number is observed to decrease with translational temperature and approaches the rotational relaxation number in the high temperature region. The rotational and vibrational relaxation numbers are generally larger in the N{sub 2} + N{sub 2} system. MD-quasi-classical trajectory (QCT) with the PESs is also used to calculate the V-T transition cross sections, the collision cross section, and the dissociation cross section for each collision pair. Direct simulation Monte Carlo (DSMC) results for hypersonic flow over a blunt body with the total collision cross section from MD/QCT simulations, Larsen-Borgnakke with new relaxation numbers, and the N{sub 2} dissociation rate from MD/QCT show a profile with a decreased translational temperature and a rotational temperature close to vibrational temperature. The results demonstrate that many of the physical models employed in DSMC should be revised as fundamental potential energy surfaces suitable for high temperature conditions become available.
Wu, Stephen; Angelikopoulos, Panagiotis; Tauriello, Gerardo; Papadimitriou, Costas; Koumoutsakos, Petros
2016-12-28
We propose a hierarchical Bayesian framework to systematically integrate heterogeneous data for the calibration of force fields in Molecular Dynamics (MD) simulations. Our approach enables the fusion of diverse experimental data sets of the physico-chemical properties of a system at different thermodynamic conditions. We demonstrate the value of this framework for the robust calibration of MD force-fields for water using experimental data of its diffusivity, radial distribution function, and density. In order to address the high computational cost associated with the hierarchical Bayesian models, we develop a novel surrogate model based on the empirical interpolation method. Further computational savings are achieved by implementing a highly parallel transitional Markov chain Monte Carlo technique. The present method bypasses possible subjective weightings of the experimental data in identifying MD force-field parameters.
Bayesian molecular design with a chemical language model.
Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo
2017-03-09
The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.
Aso, Y; Yoshioka, S; Kojima, S
2000-03-01
Isothermal crystallization of amorphous nifedipine, phenobarbital, and flopropione was studied at temperatures above and below their glass transition temperatures (T(g)). A sharp decrease in the crystallization rate with decreasing temperature was observed for phenobarbital and flopropione, such that no crystallization was observed at temperatures 20-30 degrees C lower than their T(g) within ordinary experimental time periods. In contrast, the crystallization rate of nifedipine decreased moderately with decreasing temperature, and considerable crystallization was observed at 40 degrees C below its T(g) within 4 months. The molecular mobility of these amorphous drugs was assessed by enthalpy relaxation and (1)H-NMR relaxation measurements. The enthalpy relaxation time of nifedipine was smaller than that of phenobarbital or flopropinone at the same T - T(g) values, suggesting higher molecular mobility of nifedipine. The spin-lattice relaxation time in the rotating frame (T(1rho)) decreased markedly at temperature above T(g). The slope of the Arrhenius type plot of the T(1rho) for nifedipine protons changed at about 10 degrees C below the T(g), whereas the slope for phenobarbital protons became discontinuous at about 10 degrees C above the T(g). Even at temperatures below its T(g), the spin-spin relaxation process of nifedipine could be described by the sum of its Gaussian relaxation, which is characteristic of solid protons, and its Lorentzian relaxation, which is characteristic of protons with higher mobility. In contrast, no Lorentzian relaxation was observed for phenobarbital or flopropione at temperatures below their T(g). These results also suggest that nifedipine has higher molecular mobility than phenobarbital and flopropione at temperatures below T(g). The faster crystallization of nifedipine than that of phenobarbital or flopropione observed at temperatures below its T(g) may be partly ascribed to its higher molecular mobility at these temperatures.
Molecular Dynamics Simulation on Charge Transfer Relaxation between Myoglobin and Water
Institute of Scientific and Technical Information of China (English)
CHENG Wei; ZHANG Feng-Shou; ZHANG Bo-Yang; ZHOU Hong-Yu
2007-01-01
Dynamical processes of myoglobin after photon-excited charge transfer between Fe ion and surrounding water anion ale simulated by a molecular dynamics model.The roles of Coulomb interaction effect and water effect in the relaxation process are discussed.It is found that the relaxations before and after charge transfer are similar.Strong Coulomb interactions and less water mobility decrease Coulomb energy fluctuations.An extra transferred charge of Fe ion has impact on water packing with a distance up to 0.86nm.
(14)N NQR, relaxation and molecular dynamics of the explosive TNT.
Smith, John A S; Rowe, Michael D; Althoefer, Kaspar; Peirson, Neil F; Barras, Jamie
2015-10-01
Multiple pulse sequences are widely used for signal enhancement in NQR detection applications. Since the various (14)N NQR relaxation times, signal decay times and frequency of each NQR line have a major influence on detection sequence performance, it is important to characterise these parameters and their temperature variation, as fully as possible. In this paper we discuss such measurements for a number of the ν+ and ν- NQR lines of monoclinic and orthorhombic TNT and relate the temperature variation results to molecular dynamics. The temperature variation of the (14)N spin-lattice relaxation times T1 is interpreted as due to hindered rotation of the NO2 group about the C-NO2 bond with an activation energy of 89 kJ mol(-1) for the ortho and para groups of monoclinic TNT and 70 kJ mol(-1) for the para group of orthorhombic TNT.
Allnér, Olof; Foloppe, Nicolas; Nilsson, Lennart
2015-01-22
Molecular dynamics simulations of E. coli glutaredoxin1 in water have been performed to relate the dynamical parameters and entropy obtained in NMR relaxation experiments, with results extracted from simulated trajectory data. NMR relaxation is the most widely used experimental method to obtain data on dynamics of proteins, but it is limited to relatively short timescales and to motions of backbone amides or in some cases (13)C-H vectors. By relating the experimental data to the all-atom picture obtained in molecular dynamics simulations, valuable insights on the interpretation of the experiment can be gained. We have estimated the internal dynamics and their timescales by calculating the generalized order parameters (O) for different time windows. We then calculate the quasiharmonic entropy (S) and compare it to the entropy calculated from the NMR-derived generalized order parameter of the amide vectors. Special emphasis is put on characterizing dynamics that are not expressed through the motions of the amide group. The NMR and MD methods suffer from complementary limitations, with NMR being restricted to local vectors and dynamics on a timescale determined by the rotational diffusion of the solute, while in simulations, it may be difficult to obtain sufficient sampling to ensure convergence of the results. We also evaluate the amount of sampling obtained with molecular dynamics simulations and how it is affected by the length of individual simulations, by clustering of the sampled conformations. We find that two structural turns act as hinges, allowing the α helix between them to undergo large, long timescale motions that cannot be detected in the time window of the NMR dipolar relaxation experiments. We also show that the entropy obtained from the amide vector does not account for correlated motions of adjacent residues. Finally, we show that the sampling in a total of 100 ns molecular dynamics simulation can be increased by around 50%, by dividing the
A hierarchical Bayesian framework for force field selection in molecular dynamics simulations.
Wu, S; Angelikopoulos, P; Papadimitriou, C; Moser, R; Koumoutsakos, P
2016-02-13
We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.
A Bayesian blind survey for cold molecular gas in the Universe
Lentati, Lindley; Alexander, Paul; Walter, Fabian; Decarli, Roberto
2014-01-01
A new Bayesian method for performing an image domain search for line-emitting galaxies is presented. The method uses both spatial and spectral information to robustly determine the source properties, employing either simple Gaussian, or other physically motivated models whilst using the evidence to determine the probability that the source is real. In this paper, we describe the method, and its application to both a simulated data set, and a blind survey for cold molecular gas using observations of the Hubble Deep Field North taken with the Plateau de Bure Interferometer. We make a total of 6 robust detections in the survey, 5 of which have counterparts in other observing bands. We identify the most secure detections found in a previous investigation, while finding one new probable line source with an optical ID not seen in the previous analysis. This study acts as a pilot application of Bayesian statistics to future searches to be carried out both for low-$J$ CO transitions of high redshift galaxies using th...
Nishikawa, Sadakatsu; Kamimura, Eri
2011-02-03
Ultrasonic absorption coefficients in the frequency range of 0.8-220 MHz have been measured in aqueous solution of amitriptyline (3-(10,11-dihydro-5H-dibenzo[a,d]cycloheptene-5-ylidene)-N,N-dimethyl-1-propanamine) in the concentration range from 0.20 to 0.60 mol dm(-3) at 25 °C. A single relaxational phenomenon has been observed, and the relaxation frequency is independent of the concentration. It has been also observed that the amplitude of the relaxational absorption increases linearly with the analytical concentration. From these ultrasonic relaxation data, it has been concluded that the relaxation is associated with a unimolecular reaction due to a conformational change of the solute molecule, such as a structural change due to a rotational motion of a group in the solute molecule. Molecular orbital semiempirical methods using AM1 (Austin model 1) and PM3 (modified neglect of diatomic overlap parametric method 3) have been applied to obtain the standard enthalpy of formation for amitriptyline molecule at various dihedral angles around one of the bonds in alkylamine side chain. The results have shown the two clear minimum standard enthalpies of formation for amitriptyline. From the difference of the two values, the standard enthalpy change between the two stable conformers has been calculated be 2.9 kJ mol(-1). On a rough assumption that the standard enthalpy change reflects the standard free energy change, the equilibrium constant for the rotational isomers has been estimated to be 0.31. Combining this value with the experimental ultrasonic relaxation frequency, the backward and forward rate constants have been evaluated. The standard enthalpy change of the reaction has been also estimated from the concentration dependence of the maximum absorption per wavelength, and it has been close to that calculated by the semiempirical methods. The ultrasonic absorption measurements have been also carried out in amitriptyline solution in the presence of
Dielectric relaxation in ionic liquid/dipolar solvent binary mixtures: A semi-molecular theory
Daschakraborty, Snehasis; Biswas, Ranjit
2016-03-01
A semi-molecular theory is developed here for studying dielectric relaxation (DR) in binary mixtures of ionic liquids (ILs) with common dipolar solvents. Effects of ion translation on DR time scale, and those of ion rotation on conductivity relaxation time scale are explored. Two different models for the theoretical calculations have been considered: (i) separate medium approach, where molecularities of both the IL and dipolar solvent molecules are retained, and (ii) effective medium approach, where the added dipolar solvent molecules are assumed to combine with the dipolar ions of the IL, producing a fictitious effective medium characterized via effective dipole moment, density, and diameter. Semi-molecular expressions for the diffusive DR times have been derived which incorporates the effects of wavenumber dependent orientational static correlations, ion dynamic structure factors, and ion translation. Subsequently, the theory has been applied to the binary mixtures of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) with water (H2O), and acetonitrile (CH3CN) for which experimental DR data are available. On comparison, predicted DR time scales show close agreement with the measured DR times at low IL mole fractions (xIL). At higher IL concentrations (xIL > 0.05), the theory over-estimates the relaxation times and increasingly deviates from the measurements with xIL, deviation being the maximum for the neat IL by almost two orders of magnitude. The theory predicts negligible contributions to this deviation from the xIL dependent collective orientational static correlations. The drastic difference between DR time scales for IL/solvent mixtures from theory and experiments arises primarily due to the use of the actual molecular volume ( Vmol dip ) for the rotating dipolar moiety in the present theory and suggests that only a fraction of Vmol dip is involved at high xIL. Expectedly, nice agreement between theory and experiments appears when experimental
Dielectric relaxation in ionic liquid/dipolar solvent binary mixtures: A semi-molecular theory.
Daschakraborty, Snehasis; Biswas, Ranjit
2016-03-14
A semi-molecular theory is developed here for studying dielectric relaxation (DR) in binary mixtures of ionic liquids (ILs) with common dipolar solvents. Effects of ion translation on DR time scale, and those of ion rotation on conductivity relaxation time scale are explored. Two different models for the theoretical calculations have been considered: (i) separate medium approach, where molecularities of both the IL and dipolar solvent molecules are retained, and (ii) effective medium approach, where the added dipolar solvent molecules are assumed to combine with the dipolar ions of the IL, producing a fictitious effective medium characterized via effective dipole moment, density, and diameter. Semi-molecular expressions for the diffusive DR times have been derived which incorporates the effects of wavenumber dependent orientational static correlations, ion dynamic structure factors, and ion translation. Subsequently, the theory has been applied to the binary mixtures of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) with water (H2O), and acetonitrile (CH3CN) for which experimental DR data are available. On comparison, predicted DR time scales show close agreement with the measured DR times at low IL mole fractions (x(IL)). At higher IL concentrations (x(IL) > 0.05), the theory over-estimates the relaxation times and increasingly deviates from the measurements with x(IL), deviation being the maximum for the neat IL by almost two orders of magnitude. The theory predicts negligible contributions to this deviation from the x(IL) dependent collective orientational static correlations. The drastic difference between DR time scales for IL/solvent mixtures from theory and experiments arises primarily due to the use of the actual molecular volume (V(mol)(dip)) for the rotating dipolar moiety in the present theory and suggests that only a fraction of V(mol)(dip) is involved at high x(IL). Expectedly, nice agreement between theory and experiments appears when
Grain Boundary Relaxation in Bi-Crystals: Mechanical Spectroscopy and Molecular Dynamics Simulations
Directory of Open Access Journals (Sweden)
Maier A.-K.
2015-04-01
Full Text Available Different Au-Ag-Cu samples have been studied by mechanical spectroscopy. Both polycrystals and bi-crystals show a relaxation peak at 800 K, accompanied by an elastic modulus change. Since this peak is absent in single crystals it is related to the presence of grain boundaries. Molecular dynamics simulations reveal two microscopic mechanisms, when a shear stress is applied onto a Σ5 grain boundary: at 700 K, the boundary migrates perpendicularly to the boundary plane under an external stress. At 1000 K, only sliding at the boundary is observed. These two mechanisms acting in different temperature intervals are used to model the mechanic response of a polycrystal under an applied stress. The models yield expressions for the relaxation strength Δ and for the relaxation time τ as a function of the grain size. A comparison with the mechanical spectroscopy measurements of polycrystals and the bi-crystals show that the grain boundary sliding model reproduces correctly the characteristics of the grain boundary peak.
Energy Technology Data Exchange (ETDEWEB)
Calandrini, V. [Centre de Biophysique Moleculaire, Rue Charles Sadron, 45071 Orleans (France); Synchrotron Soleil, L' Orme de Merisiers, B.P. 48, 91192 Gif-sur-Yvette (France); Hamon, V. [Centre de Biophysique Moleculaire, Rue Charles Sadron, 45071 Orleans (France); Hinsen, K. [Centre de Biophysique Moleculaire, Rue Charles Sadron, 45071 Orleans (France); Synchrotron Soleil, L' Orme de Merisiers, B.P. 48, 91192 Gif-sur-Yvette (France); Calligari, P. [Centre de Biophysique Moleculaire, Rue Charles Sadron, 45071 Orleans (France); Institut Laue-Langevin, 6 Rue Jules Horowitz, B.P. 156, 38042 Grenoble (France); Laboratoire Leon Brillouin, CEA Saclay, 91191 Gif-sur-Yvette (France); Bellissent-Funel, M.-C. [Laboratoire Leon Brillouin, CEA Saclay, 91191 Gif-sur-Yvette (France); Kneller, G.R. [Centre de Biophysique Moleculaire, Rue Charles Sadron, 45071 Orleans (France); Synchrotron Soleil, L' Orme de Merisiers, B.P. 48, 91192 Gif-sur-Yvette (France)], E-mail: kneller@cnrs-orleans.fr
2008-04-18
This paper presents a study of the influence of non-denaturing hydrostatic pressure on the relaxation dynamics of lysozyme in solution, which combines molecular dynamics simulations and quasielastic neutron scattering experiments. We compare results obtained at ambient pressure and at 3 kbar. Experiments have been performed at pD 4.6 and at a protein concentration of 60 mg/ml. For both pressures we checked the monodispersity of the protein solution by small angle neutron scattering. To interpret the simulation results and the experimental data, we adopt the fractional Ornstein-Uhlenbeck process as a model for the internal relaxation dynamics of the protein. On the experimental side, global protein motions are accounted for by the model of free translational diffusion, neglecting the much slower rotational diffusion. We find that the protein dynamics in the observed time window from about 1 to 100 ps is slowed down under pressure, while its fractal characteristics is preserved, and that the amplitudes of the motions are reduced by about 20%. The slowing down of the relaxation is reduced with increasing q-values, where more localized motions are seen.
Conversion of an atomic to a molecular argon ion and low pressure argon relaxation
M, N. Stankov; A, P. Jovanović; V, Lj Marković; S, N. Stamenković
2016-01-01
The dominant process in relaxation of DC glow discharge between two plane parallel electrodes in argon at pressure 200 Pa is analyzed by measuring the breakdown time delay and by analytical and numerical models. By using the approximate analytical model it is found that the relaxation in a range from 20 to 60 ms in afterglow is dominated by ions, produced by atomic-to-molecular conversion of Ar+ ions in the first several milliseconds after the cessation of the discharge. This conversion is confirmed by the presence of double-Gaussian distribution for the formative time delay, as well as conversion maxima in a set of memory curves measured in different conditions. Finally, the numerical one-dimensional (1D) model for determining the number densities of dominant particles in stationary DC glow discharge and two-dimensional (2D) model for the relaxation are used to confirm the previous assumptions and to determine the corresponding collision and transport coefficients of dominant species and processes. Project supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. ON171025).
A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation.
Oliver, Antoni; Canals, Vincent; Rosselló, Josep L
2017-03-06
Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction. The model can be used to quickly identify active compounds in huge databases based on the estimation of the molecule's pairing energies. The greatest molecular polar regions along with its geometrical distribution are considered by using a short set of smart energy vectors. The model is tested using similarity searches within the Directory of Useful Decoys (DUD) database. The results obtained are considerably better than previously published models. As a Target prediction methodology we propose the use of a Bayesian Classifier that uses a combination of different active compounds to build an energy-dependent probability distribution function for each target.
A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation
Oliver, Antoni; Canals, Vincent; Rosselló, Josep L.
2017-03-01
Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction. The model can be used to quickly identify active compounds in huge databases based on the estimation of the molecule’s pairing energies. The greatest molecular polar regions along with its geometrical distribution are considered by using a short set of smart energy vectors. The model is tested using similarity searches within the Directory of Useful Decoys (DUD) database. The results obtained are considerably better than previously published models. As a Target prediction methodology we propose the use of a Bayesian Classifier that uses a combination of different active compounds to build an energy-dependent probability distribution function for each target.
Microphase structures and 13C NMR relaxation parameters in ultrahigh molecular weight polyethylene
Institute of Scientific and Technical Information of China (English)
朱清仁; 洪昆仑; 鲁非; 戚嵘嵘; 庞文民; 周贵恩; 宋名实
1995-01-01
The phase transformations in ultrahigh molecular weight polyethylene（UHMWPE）gel-filmsupon superdrawing have been studied by X-ray diffraction and high resolution solid state 13C NMR.Themorphological change and molecular motions in the crystalline phase,amorphous phase and interphase are dis-cussed according to the 13C nuclear relaxation time(T1c,T2cresults.A brief interpretation to the three orfour T1cvalues in the crystalline phase is presented.It is found that the component with the highest T1c(T1cα)plays a key role in the forming of ’Shish-Kebab’ microfibril which determines the sample strength andmodulus,namely,the greater the T1cα,the higher the modulus and strength of the drawn UHMWPEgel-film.These results support the ’Shish-Kebabs’ model in crystalline polymers.
Inhibited, Explosive and Anisotropic Relaxation in a Gas of Molecular Super-Rotors
Khodorkovsky, Yuri; Hartmann, Jean-Michel; Averbukh, Ilya Sh
2015-01-01
Recently, several femtosecond laser techniques have been developed that are capable of bringing gas molecules to extremely fast rotation in a very short time, while keeping their translational motion intact and relatively slow. We investigate collisional equilibration dynamics of this new state of molecular gases, and find that it follows a remarkable generic scenario. The route to equilibrium starts with a durable metastable 'gyroscopic stage', in the course of which the molecules maintain their fast rotation and orientation of the angular momentum through many collisions. The inhibited rotational-translational relaxation is characterized by a persistent anisotropy in the molecular angular distribution, and is manifested in the long-lasting optical birefringence, and anisotropic diffusion in the gas. After a certain induction time, the 'gyroscopic stage' is abruptly terminated by a self-accelerating explosive rotational-translational energy exchange leading the gas towards the final thermal equilibrium. We i...
Indian Academy of Sciences (India)
J Colmenero; A Arbe; F Alvarez; A Narros; D Richter; M Monkenbush; B Farago
2004-07-01
The combination of molecular dynamics simulations and neutron scattering measurements on three different glass-forming polymers (polyisoprene, poly(vinyl ethylene) and polybutadiene) has allowed to establish the existence of a crossover from Gaussian to non-Gaussian behavior for the incoherent scattering function in the -relaxation regime. The deviation from Gaussian behavior observed can be reproduced assuming the existence of a distribution of discrete jump lengths underlying the sublinear diffusion of the atomic motions during the structural relaxation.
Energy Technology Data Exchange (ETDEWEB)
Tacke, Christian
2015-07-01
Multi spin systems with spin 1/2 nuclei and dipolar coupled quadrupolar nuclei can show so called ''quadrupolar dips''. There are two main reasons for this behavior: polarization transfer and relaxation. They look quite alike and without additional research cannot be differentiated easily in most cases. These two phenomena have quite different physical and theoretical backgrounds. For no or very slow dynamics, polarization transfer will take place, which is energy conserving inside the spin system. This effect can entirely be described using quantum mechanics on the spin system. Detailed knowledge about the crystallography is needed, because this affects the relevant hamiltonians directly. For systems with fast enough dynamics, relaxation takes over, and the energy flows from the spin system to the lattice; thus a more complex theoretical description is needed. This description has to include a dynamic model, usually in the form of a spectral density function. Both models should include detailed modelling of the complete spin system. A software library was developed to be able to model complex spin systems. It allows to simulate polarization transfer or relaxation effects. NMR measurements were performed on the protonic conductor K{sub 3}H(SO{sub 4}){sub 2}. A single crystal shows sharp quadrupolar dips at room temperature. Dynamics could be excluded using relaxation measurements and literature values. Thus, a polarization transfer analysis was used to describe those dips with good agreement. As a second system, imidazolium based molecular crystals were analyzed. The quadrupolar dips were expected to be caused by polarization transfer; this was carefully analyzed and found not to be true. A relaxation based analysis shows good agreement with the measured data in the high temperature area. It leverages a two step spectral density function, which indicates two distinct dynamic processes happening in this system.
Molecular-dynamics study of amorphous SiO{sub 2} relaxation
Energy Technology Data Exchange (ETDEWEB)
Fadhilah, Irfan Muhammad, E-mail: irfanmuhammadf@ymail.com [Department of Physics, Universitas Padjadjaran, Jatinangor, Sumedang 45363 (Indonesia); Rosandi, Yudi, E-mail: rosandi@geophys.unpad.ac.id [Theoretical and Computational Geophysics Laboratory, Department of Physics, Universitas Padjadjaran, Jatinangor, Sumedang 45363 (Indonesia)
2015-09-30
Using Molecular-Dynamics simulation we observed the generation of amorphous SiO{sub 2} target from a randomly distributed Si and O atoms. We applied a sequence of annealing of the target with various temperature and quenching to room temperature. The relaxation time required by the system to form SiO{sub 4} tetrahedral mesh after a relatively long simulation time, is studied. The final amorphous target was analyzed using the radial distribution function method, which can be compared with the available theoretical and experimental data. We found that up to 70% of the target atoms form the tetrahedral SiO{sub 4} molecules. The number of formed tetrahedral increases following the growth function and the rate of SiO{sub 4} formation follows Arrhenius law, depends on the annealing temperature. The local structure of amorphous SiO{sub 2} after this treatment agrees well with those reported in some literatures.
Poulsen, Jens Aage; Rossky, Peter J.
2001-11-01
We present a method based on centroid molecular dynamics (CMD) to calculate nonlinear quantum force correlation functions important in the golden rule approach for studying vibrational energy relaxation (VER) in condensed phases. We consider a model of a diatomic molecule in a two-dimensional neon liquid and also a diatomic coupled to a small Helium cluster. The predictions of the theory for the neon bath are compared and found in close agreement with available theories for VER based on the Egelstaff correction factor and Feynman-Kleinert variational theory. For the Helium cluster, the force spectrum obtained from CMD is found to be in slightly better agreement with the exact result than a method based on a cumulant approach. The results support the use of CMD in condensed phase studies of VER when quantum effects are important.
P. Arosio; M. Corti; Mariani, M; Orsini, F.; Bogani, L.; A. CANESCHI; Lago, J.; Lascialfari, A.
2015-01-01
The spin dynamics of the molecular magnetic chain [Dy(hfac)(3){NIT(C6H4OPh)}] were investigated by means of the Muon Spin Relaxation (mu+SR) technique. This system consists of a magnetic lattice of alternating Dy(III) ions and radical spins, and exhibits single-chain-magnet behavior. The magnetic properties of [Dy(hfac)(3){NIT(C6H4OPh)}] have been studied by measuring the magnetization vs. temperature at different applied magnetic fields (H - 5, 3500, and 16500 Oe) and by performing mu+SR exp...
Indian Academy of Sciences (India)
A C Ribeiro; P J Sebastiao; C Cruz
2003-08-01
We present in this work a review concerning wide frequency range 1 proton NMR relaxation studies performed in compounds exhibiting columnar mesophases, namely the Colho mesophase in the case of a liquid crystal of discotic molecules and the h mesophase in the case of a liquid crystal of biforked molecules. These NMR relaxation studies were performed combining conventional and fast ﬁeld cycling NMR techniques in a frequency range between 100 Hz and 300 MHz. The possibility of probing such a large frequency range has provided a way to effectively distinguish the inﬂuence, on the 1 relaxation proﬁles, of the different molecular movements observed in this type of mesophases. In addition, we present a comparison between the molecular dynamics in columnar (h) and lamellar (SmC) mesophases exhibited by the same biforked compound.
Uncovering Molecular Relaxation Processes with Nonlinear Spectroscopies in the Deep UV
West, Brantley Andrew
Conical intersections mediate internal conversion dynamics that compete with even the fastest nuclear motions in molecular systems. Traditional kinetic models do not apply in this regime of commensurate electronic and nuclear motion because the surroundings do not maintain equilibrium throughout the relaxation process. This dissertation focuses on uncovering the physics associated with vibronic interactions at conical intersections. Of particular interest are coherent nuclear motions driven by steep excited state potential energy gradients. Technical advances have only recently made these dynamics accessible in many systems including DNA nucleobases and cyclic polyene molecules. Optical analogues of multidimensional NMR spectroscopies have recently yielded transformative insight in relaxation processes ranging from energy transfer in photosynthesis to bond making and breaking in liquids. Prior to the start of this research, such experiments had only been conducted at infrared and visible wavelengths. Applications in the ultraviolet were motivated by studies of numerous biological systems (e.g., DNA, proteins), but had been challenged by technical issues. The work presented in this dissertation combines pulse generation techniques developed in the optical physics community with spectroscopic techniques largely pioneered by physical chemists to implement two-dimensional ultraviolet spectroscopy (2DUV). This technique is applied at the shortest wavelengths and with the best signal-to-noise ratios reported to date. Sub-picosecond excited state deactivation processes provide photo stability to the DNA double helix. Vibrational energy transfer from the solute to surrounding solvent enables relaxation of the highly non-equilibrium ground state produced by fast internal conversion. In this dissertation, nonlinear spectroscopies carried out at cryogenic temperatures are used to uncover the particular nuclear modes in the solvent that primarily accept vibrational energy from
Bayesian ensemble refinement by replica simulations and reweighting.
Hummer, Gerhard; Köfinger, Jürgen
2015-12-28
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.
Energy Technology Data Exchange (ETDEWEB)
Xie, Wen Jun; Yang, Yi Isaac; Gao, Yi Qin, E-mail: gaoyq@pku.edu.cn [Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering and Biodynamic Optical Imaging Center, Peking University, Beijing 100871 (China)
2015-12-14
In this study, we examine how complex ions such as oxyanions influence the dynamic properties of water and whether differences exist between simple halide anions and oxyanions. Nitrate anion is taken as an example to investigate the hydration properties of oxyanions. Reorientation relaxation of its hydration water can occur through two different routes: water can either break its hydrogen bond with the nitrate to form one with another water or switch between two oxygen atoms of the same nitrate. The latter molecular mechanism increases the residence time of oxyanion’s hydration water and thus nitrate anion slows down the translational motion of neighbouring water. But it is also a “structure breaker” in that it accelerates the reorientation relaxation of hydration water. Such a result illustrates that differences do exist between the hydration of oxyanions and simple halide anions as a result of different molecular geometries. Furthermore, the rotation of the nitrate solute is coupled with the hydrogen bond rearrangement of its hydration water. The nitrate anion can either tilt along the axis perpendicularly to the plane or rotate in the plane. We find that the two reorientation relaxation routes of the hydration water lead to different relaxation dynamics in each of the two above movements of the nitrate solute. The current study suggests that molecular geometry could play an important role in solute hydration and dynamics.
Magnetocaloric effect in Mn2-pyrazole-[Nb(CN)8] molecular magnet by relaxation calorimetry
Pełka, R.; Gajewski, M.; Miyazaki, Y.; Yamashita, S.; Nakazawa, Y.; Fitta, M.; Pinkowicz, D.; Sieklucka, B.
2016-12-01
Magnetocaloric effect in {[Mn(pyrazole)4]2[Nb(CN)8]·4 H2O}n molecular magnet is reported. It crystallizes in tetragonal I41/a space group. The compound exhibits a phase transition to a long range magnetically ordered state at TN ≈ 22.8 K. Temperature dependences of the magnetic entropy change ΔSM as well as the adiabatic temperature change ΔTad due to applied field change μ0 ΔH in the range of 0.1-9 T have been inferred from the relaxation calorimetry measurements. A systematic approximate approach has been used to determine the lattice contribution to the heat capacity. The maximum value of ΔSM for μ0 ΔH = 5 T is 6.83 J mol-1 K-1 (6.65 J kg-1 K-1) at 24.3 K. The corresponding maximum value of ΔTad is 1.4 K at 23.8 K. The temperature dependence of the exponent n characterizing the field dependence of ΔSM has been estimated. It attains the value of 0.64 at the transition temperature, which is consistent with the 3D Heisenberg universality class. A hitherto unobserved two-peak structure has been revealed in the temperature dependence of ΔTad.
Relaxed molecular clock provides evidence for long-distance dispersal of Nothofagus (southern beech.
Directory of Open Access Journals (Sweden)
Michael Knapp
2005-01-01
Full Text Available Nothofagus (southern beech, with an 80-million-year-old fossil record, has become iconic as a plant genus whose ancient Gondwanan relationships reach back into the Cretaceous era. Closely associated with Wegener's theory of "Kontinentaldrift", Nothofagus has been regarded as the "key genus in plant biogeography". This paradigm has the New Zealand species as passengers on a Moa's Ark that rafted away from other landmasses following the breakup of Gondwana. An alternative explanation for the current transoceanic distribution of species seems almost inconceivable given that Nothofagus seeds are generally thought to be poorly suited for dispersal across large distances or oceans. Here we test the Moa's Ark hypothesis using relaxed molecular clock methods in the analysis of a 7.2-kb fragment of the chloroplast genome. Our analyses provide the first unequivocal molecular clock evidence that, whilst some Nothofagus transoceanic distributions are consistent with vicariance, trans-Tasman Sea distributions can only be explained by long-distance dispersal. Thus, our analyses support the interpretation of an absence of Lophozonia and Fuscospora pollen types in the New Zealand Cretaceous fossil record as evidence for Tertiary dispersals of Nothofagus to New Zealand. Our findings contradict those from recent cladistic analyses of biogeographic data that have concluded transoceanic Nothofagus distributions can only be explained by vicariance events and subsequent extinction. They indicate that the biogeographic history of Nothofagus is more complex than envisaged under opposing polarised views expressed in the ongoing controversy over the relevance of dispersal and vicariance for explaining plant biodiversity. They provide motivation and justification for developing more complex hypotheses that seek to explain the origins of Southern Hemisphere biota.
Rangel, David P; Baveye, Philippe C; Robinson, Bruce H
2012-06-07
We simulate spin relaxation processes, which may be measured by either continuous wave or pulsed magnetic resonance techniques, using trajectory-based simulation methodologies. The spin-lattice relaxation rates are extracted numerically from the relaxation simulations. The rates obtained from the numerical fitting of the relaxation curves are compared to those obtained by direct simulation from the relaxation Bloch-Wangsness-Abragam-Redfield theory (BWART). We have restricted our study to anisotropic rigid-body rotational processes, and to the chemical shift anisotropy (CSA) and a single spin-spin dipolar (END) coupling mechanisms. Examples using electron paramagnetic resonance (EPR) nitroxide and nuclear magnetic resonance (NMR) deuterium quadrupolar systems are provided. The objective is to compare those rates obtained by numerical simulations with the rates obtained by BWART. There is excellent agreement between the simulated and BWART rates for a Hamiltonian describing a single spin (an electron) interacting with the bath through the chemical shift anisotropy (CSA) mechanism undergoing anisotropic rotational diffusion. In contrast, when the Hamiltonian contains both the chemical shift anisotropy (CSA) and the spin-spin dipolar (END) mechanisms, the decay rate of a single exponential fit of the simulated spin-lattice relaxation rate is up to a factor of 0.2 smaller than that predicted by BWART. When the relaxation curves are fit to a double exponential, the slow and fast rates extracted from the decay curves bound the BWART prediction. An extended BWART theory, in the literature, includes the need for multiple relaxation rates and indicates that the multiexponential decay is due to the combined effects of direct and cross-relaxation mechanisms.
Institute of Scientific and Technical Information of China (English)
Hou Quan-Wen; Cao Bing-Yang
2012-01-01
The phonon relaxation and heat conduction in one-dimensional Fermi-Pasta-Ulam (FPU) β lattices are studied by using molecular dynamics simulations.The phonon relaxation rate,which dominates the length dependence of the FPU β lattice,is first calculated from the energy autocorrelation function for different modes at various temperatures through equilibrium molecular dynamics simulations.We find that the relaxation rate as a function of wave number k is proportional to k1.688,which leads to a N0.41 divergence of the thermal conductivity in the framework of Green-Kubo relation.This is also in good agreement with the data obtained by non-equilibrium molecular dynamics simulations which estimate the length dependence exponent of the thermal conductivity as 0.415.Our results confirm the N2/5divergence in one-dimensional FPU β lattices.The effects of the heat flux on the thermal conductivity are also studied by imposing different temperature differences on the two ends of the lattices.We find that the thermal conductivity is insensitive to the heat flux under our simulation conditions.It implies that the linear response theory is applicable towards the heat conduction in one-dimensional FPUβ lattices.
Energy Technology Data Exchange (ETDEWEB)
Liu, Qing; Shi, Chaowei [Hefei National Laboratory for Physical Sciences at The Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230026 (China); Yu, Lu [Hefei National Laboratory for Physical Sciences at The Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230026 (China); High Magnetic Field Laboratory, Chinese Academy of Science, Hefei, Anhui, 230031 (China); Zhang, Longhua [Hefei National Laboratory for Physical Sciences at The Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230026 (China); Xiong, Ying, E-mail: yxiong73@ustc.edu.cn [Hefei National Laboratory for Physical Sciences at The Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230026 (China); Tian, Changlin, E-mail: cltian@ustc.edu.cn [Hefei National Laboratory for Physical Sciences at The Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230026 (China); High Magnetic Field Laboratory, Chinese Academy of Science, Hefei, Anhui, 230031 (China)
2015-02-13
Internal backbone dynamic motions are essential for different protein functions and occur on a wide range of time scales, from femtoseconds to seconds. Molecular dynamic (MD) simulations and nuclear magnetic resonance (NMR) spin relaxation measurements are valuable tools to gain access to fast (nanosecond) internal motions. However, there exist few reports on correlation analysis between MD and NMR relaxation data. Here, backbone relaxation measurements of {sup 15}N-labeled SH3 (Src homology 3) domain proteins in aqueous buffer were used to generate general order parameters (S{sup 2}) using a model-free approach. Simultaneously, 80 ns MD simulations of SH3 domain proteins in a defined hydrated box at neutral pH were conducted and the general order parameters (S{sup 2}) were derived from the MD trajectory. Correlation analysis using the Gromos force field indicated that S{sup 2} values from NMR relaxation measurements and MD simulations were significantly different. MD simulations were performed on models with different charge states for three histidine residues, and with different water models, which were SPC (simple point charge) water model and SPC/E (extended simple point charge) water model. S{sup 2} parameters from MD simulations with charges for all three histidines and with the SPC/E water model correlated well with S{sup 2} calculated from the experimental NMR relaxation measurements, in a site-specific manner. - Highlights: • Correlation analysis between NMR relaxation measurements and MD simulations. • General order parameter (S{sup 2}) as common reference between the two methods. • Different protein dynamics with different Histidine charge states in neutral pH. • Different protein dynamics with different water models.
Influence of hydroxypropyl cellulose on molecular relaxations of epoxy-amine networks
Directory of Open Access Journals (Sweden)
Maria Inez G. Miranda
2013-01-01
Full Text Available A dynamic mechanical analysis (DMTA study was conducted on epoxy-amine networks crosslinked in the presence of low contents of hydroxypropyl cellulose (HPC. The epoxy resin chosen was diglycidylether of bisphenol-A (DGEBA and the crosslinker was 4,4'-diaminodiphenylmethane (DDM. In the glassy region, primary (α and secondary (β, γ relaxations originating from the epoxy and HPC components were well detected. Two primary relaxations of neat epoxy and epoxy/HPC systems, referred to as αepoxy and α'epoxy, could be detected, showing a particular glassy behavior for the systems studied in comparison with systems cured in bulk. The main relaxation temperature Tα (at the peak of αepoxy relaxation of the epoxy systems increased slightly with the addition of HPC. The activation energy for this transition (Tα of the epoxy-amine networks was determined both from tan δ and the peak temperatures for the loss modulus measured at various frequencies. The activation energy of the αepoxy relaxation determined from the loss modulus was more reliable than that based on tan δ. The addition of HPC lowered the activation energy of this αepoxy relaxation.
Sichani, Mehrdad M.; Spearot, Douglas E.
2016-07-01
The molecular dynamics simulation method is used to investigate the dependence of crystal orientation and shock wave strength on dislocation density evolution in single crystal Cu. Four different shock directions , , , and are selected to study the role of crystal orientation on dislocation generation immediately behind the shock front and plastic relaxation as the system reaches the hydrostatic state. Dislocation density evolution is analyzed for particle velocities between the Hugoniot elastic limit ( up H E L ) for each orientation up to a maximum of 1.5 km/s. Generally, dislocation density increases with increasing particle velocity for all shock orientations. Plastic relaxation for shock in the , , and directions is primarily due to a reduction in the Shockley partial dislocation density. In addition, plastic anisotropy between these orientations is less apparent at particle velocities above 1.1 km/s. In contrast, plastic relaxation is limited for shock in the orientation. This is partially due to the emergence of sessile stair-rod dislocations with Burgers vectors of 1/3 and 1/6. The nucleation of 1/6 dislocations at lower particle velocities is mainly due to the reaction between Shockley partial dislocations and twin boundaries. On the other hand, for the particle velocities above 1.1 km/s, the nucleation of 1/3 dislocations is predominantly due to reaction between Shockley partial dislocations at stacking fault intersections. Both mechanisms promote greater dislocation densities after relaxation for shock pressures above 34 GPa compared to the other three shock orientations.
Shete, Ganesh; Khomane, Kailas S; Bansal, Arvind Kumar
2014-01-01
The purpose of this paper was to investigate the relaxation behavior of amorphous hesperetin (HRN), using dielectric spectroscopy, and assessment of its crystallization kinetics above glass transition temperature (Tg ). Amorphous HRN exhibited both local (β-) and global (α-) relaxations. β-Relaxation was observed below Tg , whereas α-relaxation prominently emerged above Tg . β-Relaxation was found to be of Johari-Goldstein type and was correlated with α-process by coupling model. Secondly, isothermal crystallization experiments were performed at 363 K (Tg + 16.5 K), 373 K (Tg + 26.5 K), and 383 K (Tg + 36.5 K). The kinetics of crystallization, obtained from the normalized dielectric strength, was modeled using the Avrami model. Havriliak-Negami (HN) shape parameters, αHN and αHN .βHN , were analyzed during the course of crystallization to understand the dynamics of amorphous phase during the emergence of crystallites. HN shape parameters indicated that long range (α-like) were motions affected to a greater extent than short range (β-like) motions during isothermal crystallization studies at all temperature conditions. The variable behavior of α-like motions at different isothermal crystallization temperatures was attributed to evolving crystallites with time and increase in electrical conductivity with temperature.
Molecular structure-property correlations from optical nonlinearity and thermal-relaxation dynamics.
Bhattacharyya, Indrajit; Priyadarshi, Shekhar; Goswami, Debabrata
2009-02-01
We apply ultrafast single beam Z-scan technique to measure saturation absorption coefficients and nonlinear-refraction coefficients of primary alcohols at 1560 nm. The nonlinear effects result from vibronic transitions and cubic nonlinear-refraction. To measure the pure total third-order nonlinear susceptibility, we removed thermal effects with a frequency optimized optical-chopper. Our measurements of thermal-relaxation dynamics of alcohols, from 1560 nm thermal lens pump and 780 nm probe experiments revealed faster and slower thermal-relaxation timescales, respectively, from conduction and convection. The faster timescale accurately predicts thermal-diffusivity, which decreases linearly with alcohol chain-lengths since thermal-relaxation is slower in heavier molecules. The relation between thermal-diffusivity and alcohol chain-length confirms structure-property relationship.
Rotational relaxation in molecular hydrogen and deuterium: Theory versus acoustic experiments
Energy Technology Data Exchange (ETDEWEB)
Montero, S., E-mail: emsalvador@iem.cfmac.csic.es [Laboratory of Molecular Fluid Dynamics @ Instituto de Estructura de la Materia, CSIC, Serrano 121, 28006 Madrid (Spain); Pérez-Ríos, J. [Physics Department, Purdue University, West Lafayette, Indiana 47907 (United States)
2014-09-21
An explicit formulation of the rotational relaxation time in terms of state-to-state rate coefficients associated to inelastic collisions is reported. The state-to-state rates needed for the detailed interpretation of relaxation in H{sub 2} and D{sub 2}, including isotopic variant mixtures, have been calculated by solving the close-coupling Schrödinger equations using the H{sub 2}–H{sub 2} potential energy surface by Diep and Johnson [J. Chem. Phys. 112, 4465 (2000)]. Relaxation related quantities (rotational effective cross section, bulk viscosity, relaxation time, and collision number) calculated from first principles agree reasonably well with acoustic absorption experimental data on H{sub 2} and D{sub 2} between 30 and 293 K. This result confirms at once the proposed formulation, and the validation of the H{sub 2}–H{sub 2} potential energy surface employed, since no approximations have been introduced in the dynamics. Accordingly, the state-to-state rates derived from Diep and Johnson potential energy surface appear to be overestimated by up to 10% for H{sub 2}, and up to 30% for D{sub 2} at T = 300 K, showing a better agreement at lower temperatures.
Thornhill, Andrew H; Popple, Lindsay W; Carter, Richard J; Ho, Simon Y W; Crisp, Michael D
2012-04-01
The identification and application of reliable fossil calibrations represents a key component of many molecular studies of evolutionary timescales. In studies of plants, most paleontological calibrations are associated with macrofossils. However, the pollen record can also inform age calibrations if fossils matching extant pollen groups are found. Recent work has shown that pollen of the myrtle family, Myrtaceae, can be classified into a number of morphological groups that are synapomorphic with molecular groups. By assembling a data matrix of pollen morphological characters from extant and fossil Myrtaceae, we were able to measure the fit of 26 pollen fossils to a molecular phylogenetic tree using parsimony optimisation of characters. We identified eight Myrtaceidites fossils as appropriate for calibration based on the most parsimonious placements of these fossils on the tree. These fossils were used to inform age constraints in a Bayesian phylogenetic analysis of a sequence alignment comprising two sequences from the chloroplast genome (matK and ndhF) and one nuclear locus (ITS), sampled from 106 taxa representing 80 genera. Three additional analyses were calibrated by placing pollen fossils using geographic and morphological information (eight calibrations), macrofossils (five calibrations), and macrofossils and pollen fossils in combination (12 calibrations). The addition of new fossil pollen calibrations led to older crown ages than have previously been found for tribes such as Eucalypteae and Myrteae. Estimates of rate variation among lineages were affected by the choice of calibrations, suggesting that the use of multiple calibrations can improve estimates of rate heterogeneity among lineages. This study illustrates the potential of including pollen-based calibrations in molecular studies of divergence times.
DEFF Research Database (Denmark)
Roed, Lisa Anita; Niss, Kristine; Jakobsen, Bo
2015-01-01
The frequency dependent specific heat has been measured under pressure for the molecular glass forming liquid 5-polyphenyl-4-ether in the viscous regime close to the glass transition. The temperature and pressure dependences of the characteristic time scale associated with the specific heat...
Directory of Open Access Journals (Sweden)
Debashis Mukherjee
2002-06-01
Full Text Available Abstract: We present in this paper two new versions of Rayleigh-SchrÃ‚Â¨odinger (RS and the Brillouin-Wigner (BW state-specific multi-reference perturbative theories (SSMRPT which stem from our state-specific multi-reference coupled-cluster formalism (SS-MRCC, developed with a complete active space (CAS. They are manifestly sizeextensive and are designed to avoid intruders. The combining coefficients cÃŽÂ¼ for the model functions ÃÂ†ÃŽÂ¼ are completely relaxed and are obtained by diagonalizing an effective operator in the model space, one root of which is the target eigenvalue of interest. By invoking suitable partitioning of the hamiltonian, very convenient perturbative versions of the formalism in both the RS and the BW forms are developed for the second order energy. The unperturbed hamiltonians for these theories can be chosen to be of both MÃÂ†ller-Plesset (MP and Epstein-Nesbet (EN type. However, we choose the corresponding Fock operator fÃŽÂ¼ for each model function ÃÂ†ÃŽÂ¼, whose diagonal elements are used to define the unperturbed hamiltonian in the MP partition. In the EN partition, we additionally include all the diagonal direct and exchange ladders. Our SS-MRPT thus utilizes a multi-partitioning strategy. Illustrative numerical applications are presented for potential energy surfaces (PES of the ground (1ÃŽÂ£+ and the first delta (1ÃŽÂ” states of CH+ which possess pronounced multi-reference character. Comparison of the results with the corresponding full CI values indicates the efficacy of our formalisms.
Message passing with relaxed moment matching
Qi, Yuan; Guo, Yandong
2012-01-01
Bayesian learning is often hampered by large computational expense. As a powerful generalization of popular belief propagation, expectation propagation (EP) efficiently approximates the exact Bayesian computation. Nevertheless, EP can be sensitive to outliers and suffer from divergence for difficult cases. To address this issue, we propose a new approximate inference approach, relaxed expectation propagation (REP). It relaxes the moment matching requirement of expectation propagation by addin...
Accelerating molecular simulations of proteins using Bayesian inference on weak information
Perez, Alberto; MacCallum, Justin L.; Dill, Ken A.
2015-01-01
Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate “weak” external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer “to form a hydrophobic core,” “to form good secondary structures,” or “to seek a compact state.” This kind of information has been too combinatoric, nonspecific, and vague to help guide MD simulations before. Within atomistic replica-exchange molecular dynamics (REMD), we develop a statistical mechanical framework, modeling using limited data with coarse physical insight(s) (MELD + CPI), for harnessing weak information. As a test, we apply MELD + CPI to predict the native structures of 20 small proteins. MELD + CPI samples to within less than 3.2 Å from native for all 20 and correctly chooses the native structures (<4 Å) for 15 of them, including ubiquitin, a millisecond folder. MELD + CPI is up to five orders of magnitude faster than brute-force MD, satisfies detailed balance, and should scale well to larger proteins. MELD + CPI may be useful where physics-based simulations are needed to study protein mechanisms and populations and where we have some heuristic or coarse physical knowledge about states of interest. PMID:26351667
Progress in the study of molecular organized assemblies by dielectric relaxation spectroscopy
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Because dielectric spectroscopy covers a great many problems in physical and chemical systems occurring in an extremely wide frequency range, the study of this method plays an important role in physical chemistry. As an effective tool to detect inner properties of substance systems, the dielectric spectroscopy method is widely used in chemical systems and has been dramatically developed in recent decade. This review paper describes the applications of the dielectric spectroscopy in the chemical field, and main concentrations are focused on the micelle, microemulsion and other so-called molecular organized assemblies. Some dielectric principles and models proposed for these systems are introduced. In addition, recent technical developments in dielectric spectroscopy and developing trend of this method in other chemical systems are reviewed.
Relaxing the Molecular Clock to Different Degrees for Different Substitution Types.
Lee, Hui-Jie; Rodrigue, Nicolas; Thorne, Jeffrey L
2015-08-01
Rates of molecular evolution can vary over time. Diverse statistical techniques for divergence time estimation have been developed to accommodate this variation. These typically require that all sequence (or codon) positions at a locus change independently of one another. They also generally assume that the rates of different types of nucleotide substitutions vary across a phylogeny in the same way. This permits divergence time estimation procedures to employ an instantaneous rate matrix with relative rates that do not differ among branches. However, previous studies have suggested that some substitution types (e.g., CpG to TpG changes in mammals) are more clock-like than others. As has been previously noted, this is biologically plausible given the mutational mechanism of CpG to TpG changes. Through stochastic mapping of sequence histories from context-independent substitution models, our approach allows for context-dependent nucleotide substitutions to change their relative rates over time. We apply our approach to the analysis of a 0.15 Mb intergenic region from eight primates. In accord with previous findings, we find comparatively little rate variation over time for CpG to TpG substitutions but we find more for other substitution types. We conclude by discussing the limitations and prospects of our approach.
Muon spin relaxation investigation of tetranuclear iron(III) Fe_4(OCH_3)_6(dpm)_6 molecular cluster
Procissi, D.; P. Arosio; Orsini, F.; Marinone, M.; Cornia, A.; Lascialfari, A.
2009-01-01
We present a study of the spin dynamics of Fe_4(OCH_3)_6(dpm)_6 single molecule magnet by means of SQUID magnetization and muon relaxation (µ^(+)SR) measurements. In longitudinal field µ^+SR experiments performed at magnetic fields H=200, 1000 Oe, the muon asymmetry P(t) could be fitted by means of three components, the first constant, the second fast relaxing through a quasiexponential decay, and the third, the slowest relaxing, showing an exponential decay. The slowest muon relaxation rate ...
Baranowski, M.; Woźniak-Braszak, A.; Jurga, K.
2016-01-01
The paper presents the benefits of using fast adiabatic passage for the study of molecular dynamics in the solid state heteronuclear systems in the laboratory frame. A homemade pulse spectrometer operating at the frequency of 30.2 MHz and 28.411 MHz for protons and fluorines, respectively, has been enhanced with microcontroller direct digital synthesizer DDS controller [1-4]. This work briefly describes how to construct a low-cost and easy-to-assemble adiabatic extension set for homemade and commercial spectrometers based on recently very popular Arduino shields. The described set was designed for fast adiabatic generation. Timing and synchronization problems are discussed. The cross-relaxation experiments with different initial states of the two spin systems have been performed. Contrary to our previous work [5] where the steady-state NOE experiments were conducted now proton spins 1H are polarized in the magnetic field B0 while fluorine spins 19F are perturbed by selective saturation for a short time and then the system is allowed to evolve for a period in the absence of a saturating field. The adiabatic passage application leads to a reversal of magnetization of fluorine spins and increases the amplitude of the signal.
Dutt, G. B.; Sachdeva, A.
2003-05-01
Rotational relaxation of three organic solutes, coumarin 6 (C6), 2,5-dimethyl-1, 4-dioxo3,6-diphenylpyrrolo[3,4-c]pyrrole (DMDPP), and nile red (NR), that are similar in size but distinct in shape has been studied in a nonpolar solvent, squalane as a function of temperature to find out how the mechanical friction experienced by the solute molecule is influenced by its shape. It has been observed that C6 rotates slowest followed by NR and DMDPP. The results are analyzed using Stokes-Einstein-Debye (SED) hydrodynamic theory and also quasihydrodynamic theories of Gierer and Wirtz, and Dote, Kivelson, and Schwartz. Analysis of the data using the SED theory reveals that the measured reorientation times of C6 and DMDPP follow subslip behavior whereas those of NR are found to match slip predictions. While no single model could mimic the observed trend even in a qualitative manner, the reorientation times of C6 and DMDPP when normalized by their respective shape factors and boundary-condition parameters can be scaled on a common curve over the entire range of temperature studied. The probable reasons for the distinctive rotational behavior of NR as compared to C6 and DMDPP are explained in terms of its molecular shape and how this in turn influences the boundary-condition parameter are discussed.
Inferring Genotype of DNA Molecular Marker by Bayesian Theorem%应用贝叶斯理论推断DNA分子标记基因型
Institute of Scientific and Technical Information of China (English)
莫惠栋; 姜长鉴
2002-01-01
引入贝叶斯理论用以从DNA分子标记的表现型(电泳谱带)推断其基因型(DNA来源).结果表明,根据标记座位独立假定而确定的遗传信息不完全标记的基因型概率,与根据邻近的遗传信息完全标记的基因型和有关重组率算得的相应贝叶斯概率,通常都有很大的差异.所以在进行数量性状基因定位和标记辅助选择等工作之前,应当计算每一个体基因组上所有遗传信息不完全座位的有关基因型的贝叶斯概率.文中列出计算未知基因型的贝叶斯概率的详细过程,也讨论了贝叶斯概率的若干推广应用.%Bayesian theorem is applied to infer the DNA molecular marker genotype(DNA chain type) from its phenotype (electrophoresis band type). The results indicated that large differences often present in the genotype probability of a molecular marker with incomplete genetic information when it is obtained from the assumption of independence among markers as compared with that inferred from the genotypes of the flanking markers with the complete genetic information and the recombination fractions among them based on the Bayesian theorem. Therefore, before utilizing the marker information, such as in mapping quantitative trait loci (QTL), marker assisted selection (MAS) etc., Bayesian probability of the genotype for all markers with incomplete genetic information must be calculated over the whole genome for every individual. This study provides detailed procedure for the calculation of the Bayesian probability of the unknown genotype. Several extensions were also discussed for the application of the Bayesian theorem.
Energy Technology Data Exchange (ETDEWEB)
Arosio, Paolo, E-mail: paolo.arosio@guest.unimi.it; Orsini, Francesco [Department of Physics, Università degli Studi di Milano, and INSTM, Milano (Italy); Corti, Maurizio [Department of Physics, Università degli Studi di Pavia and INSTM, Pavia (Italy); Mariani, Manuel [Department of Physics and Astronomy, Università degli Studi di Bologna, Bologna (Italy); Bogani, Lapo [Physikalisches Institut, Universität Stuttgart, Stuttgart (Germany); Caneschi, Andrea [INSTM and Department of Chemistry, University of Florence, Firenze (Italy); Lago, Jorge [Departamento de Quimica Inorganica, Universidad del Pais Vasco, Bilbao (Spain); Lascialfari, Alessandro [Department of Physics, Università degli Studi di Milano, and INSTM, Milano (Italy); Centro S3, Istituto Nanoscienze - CNR, Modena (Italy)
2015-05-07
The spin dynamics of the molecular magnetic chain [Dy(hfac){sub 3}(NIT(C{sub 6}H{sub 4}OPh))] were investigated by means of the Muon Spin Relaxation (μ{sup +}SR) technique. This system consists of a magnetic lattice of alternating Dy(III) ions and radical spins, and exhibits single-chain-magnet behavior. The magnetic properties of [Dy(hfac){sub 3}(NIT(C{sub 6}H{sub 4}OPh))] have been studied by measuring the magnetization vs. temperature at different applied magnetic fields (H = 5, 3500, and 16500 Oe) and by performing μ{sup +}SR experiments vs. temperature in zero field and in a longitudinal applied magnetic field H = 3500 Oe. The muon asymmetry P(t) was fitted by the sum of three components, two stretched-exponential decays with fast and intermediate relaxation times, and a third slow exponential decay. The temperature dependence of the spin dynamics has been determined by analyzing the muon longitudinal relaxation rate λ{sub interm}(T), associated with the intermediate relaxing component. The experimental λ{sub interm}(T) data were fitted with a corrected phenomenological Bloembergen-Purcell-Pound law by using a distribution of thermally activated correlation times, which average to τ = τ{sub 0} exp(Δ/k{sub B}T), corresponding to a distribution of energy barriers Δ. The correlation times can be associated with the spin freezing that occurs when the system condenses in the ground state.
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity but MoA classification in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity mode of action using a recently published dataset contain...
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently pu...
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
新家, 健精
2013-01-01
© 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
Lidorikis, Elefterios; Bachlechner, Martina E.; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya
2005-09-01
A hybrid atomistic-continuum simulation approach has been implemented to study strain relaxation in lattice-mismatched Si/Si3N4 nanopixels on a Si(111) substrate. We couple the molecular-dynamics (MD) and finite-element simulation approaches to provide an atomistic description near the interface and a continuum description deep into the substrate, increasing the accessible length scales and greatly reducing the computational cost. The results of the hybrid simulation are validated against full multimillion-atom MD simulations. We find that strain relaxation in Si/Si3N4 nanopixels may occur through the formation of a network of interfacial domain boundaries reminiscent of interfacial misfit dislocations. They result from the nucleation of domains of different interfacial bonding at the free edges and corners of the nanopixel, and subsequent to their creation they propagate inwards. We follow the motion of the domain boundaries and estimate a propagation speed of about ˜2.5×103m/s . The effects of temperature, nanopixel architecture, and film structure on strain relaxation are also investigated. We find: (i) elevated temperature increases the interfacial domain nucleation rates; (ii) a thin compliant Si layer between the film and the substrate plays a beneficial role in partially suppressing strain relaxation; and (iii) additional control over the interface morphology may be achieved by varying the film structure.
Institute of Scientific and Technical Information of China (English)
Druzhinina I S; Kubicek C P
2004-01-01
@@ The Hypocrea lixii/Trichoderma harzianum species aggregate contains a group of taxa (H. lixii/T.harzianum , T. aggressivum , T. tomentosum , T. cerinum , T. velutinum , H. tawa ) of which some (e. g. T. harzianum) are important for biocontrol of plant pathogenic fungi in agriculture, whereas others are aggressive pathogens of Agaricus spp. and Pleurotus spp. in mushroom farms (T. aggressivum), or opportunistic pathogens of immunocompromised mammals including humans (T. harzianum). We characterized the evolutionary properties of three genomic regions in Hypocrea/Trichoderma: the internal transcribed spacer regions ITS1 and 2 of rDNA, the large intron of translation elongation factor 1-alpha (tef1a), and a portion of the large exon of the endochitinase 42 gene (ech42 ), selected the best model which describes the evolution of every fragment, tested the molecular clock hypothesis and made an estimation of the usability of the combined three fragments data matrix for the phylogenetic analysis of the genus as a whole as well as on the level of the holomorphic H. liaxii/T. harzianum species clade and separate clonal lineages. To this end, we applied Bayesian phylogenetic inferences to 124 sequences of ITS1 and 2 and of the large tef1a intron, and to 64 ech42 gene sequences to resolve the evolution of H. lixii/T. harzianum with respect to the position of other taxa with closely related phenotypes. The resulting phylogram clearly identified T.aggressivum, T. velutinum, H. tawa, T. cerinum and T. tomentosum as phylogenetic species, and in addition identified three new unknown phylogenetic species as members of this clacle. The clear distinction between T. tomentosum and T. cerinum was not recognized in all trees, but was supported by multivariate analysis of phenotype micro arrays. In contrast, H. lixii/T. harzianum did not form a single phylogenetic species in this study, as its monophyly was not supported in any analysis. Strains morphologically identified as H. lixii
Glowacki, David R; Rodgers, W J; Shannon, Robin; Robertson, Struan H; Harvey, Jeremy N
2017-04-28
The extent to which vibrational energy transfer dynamics can impact reaction outcomes beyond the gas phase remains an active research question. Molecular dynamics (MD) simulations are the method of choice for investigating such questions; however, they can be extremely expensive, and therefore it is worth developing cheaper models that are capable of furnishing reasonable results. This paper has two primary aims. First, we investigate the competition between energy relaxation and reaction at 'hotspots' that form on the surface of diamond during the chemical vapour deposition process. To explore this, we developed an efficient reactive potential energy surface by fitting an empirical valence bond model to higher-level ab initio electronic structure theory. We then ran 160 000 NVE trajectories on a large slab of diamond, and the results are in reasonable agreement with experiment: they suggest that energy dissipation from surface hotspots is complete within a few hundred femtoseconds, but that a small fraction of CH3 does in fact undergo dissociation prior to the onset of thermal equilibrium. Second, we developed and tested a general procedure to formulate and solve the energy-grained master equation (EGME) for surface chemistry problems. The procedure we outline splits the diamond slab into system and bath components, and then evaluates microcanonical transition-state theory rate coefficients in the configuration space of the system atoms. Energy transfer from the system to the bath is estimated using linear response theory from a single long MD trajectory, and used to parametrize an energy transfer function which can be input into the EGME. Despite the number of approximations involved, the surface EGME results are in reasonable agreement with the NVE MD simulations, but considerably cheaper. The results are encouraging, because they offer a computationally tractable strategy for investigating non-equilibrium reaction dynamics at surfaces for a broader range of
Energy Technology Data Exchange (ETDEWEB)
Yurasov, D. V., E-mail: Inquisitor@ipm.sci-nnov.ru [Russian Academy of Sciences, Institute for Physics of Microstructures (Russian Federation); Bobrov, A. I. [Lobachevsky State University of Nizhny Novgorod (Russian Federation); Daniltsev, V. M.; Novikov, A. V. [Russian Academy of Sciences, Institute for Physics of Microstructures (Russian Federation); Pavlov, D. A. [Lobachevsky State University of Nizhny Novgorod (Russian Federation); Skorokhodov, E. V.; Shaleev, M. V.; Yunin, P. A. [Russian Academy of Sciences, Institute for Physics of Microstructures (Russian Federation)
2015-11-15
Influence of the Ge layer thickness and annealing conditions on the parameters of relaxed Ge/Si(001) layers grown by molecular beam epitaxy via two-stage growth is investigated. The dependences of the threading dislocation density and surface roughness on the Ge layer thickness, annealing temperature and time, and the presence of a hydrogen atmosphere are obtained. As a result of optimization of the growth and annealing conditions, relaxed Ge/Si(001) layers which are thinner than 1 μm with a low threading dislocation density on the order of 10{sup 7} cm{sup –2} and a root mean square roughness of less than 1 nm are obtained.
BEAST: Bayesian evolutionary analysis by sampling trees
Directory of Open Access Journals (Sweden)
Drummond Alexei J
2007-11-01
Full Text Available 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 models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.
Bernardo, Jose M
2000-01-01
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critica
Villalba, Jesús
2015-01-01
In this document we are going to derive the equations needed to implement a Variational Bayes estimation of the parameters of the simplified probabilistic linear discriminant analysis (SPLDA) model. This can be used to adapt SPLDA from one database to another with few development data or to implement the fully Bayesian recipe. Our approach is similar to Bishop's VB PPCA.
Bayesian non- and semi-parametric methods and applications
Rossi, Peter
2014-01-01
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number
Non-homogeneous dynamic Bayesian networks for continuous data
Grzegorczyk, Marco; Husmeier, Dirk
2011-01-01
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with c
Maximum margin Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael; Tschiatschek, Sebastian
2012-03-01
We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
Hedlund, Jonas
2014-01-01
This paper introduces private sender information into a sender-receiver game of Bayesian persuasion with monotonic sender preferences. I derive properties of increasing differences related to the precision of signals and use these to fully characterize the set of equilibria robust to the intuitive criterion. In particular, all such equilibria are either separating, i.e., the sender's choice of signal reveals his private information to the receiver, or fully disclosing, i.e., the outcome of th...
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...
Coffey, W. T.; Déjardin, P. M.; Walsh, M. E.
1999-03-01
Exact solutions obtained by Gross [J. Chem. Phys. 23, 1415 (1955)] and Sack [Proc. Phys. Soc. London, Sect. B 70, 402 (1957)] for the complex polarizability of assemblies of nonelectrically interacting rotators subjected to a variety of collisions and various approximations to that quantity, specifically the Rocard equation are reappraised in view of recent attempts to use a variety of forms of that equation for the interpretation of far infrared resonance absorption spectra. It is shown that for small values of the inertial parameter (heavy damping) the Rocard equation yields a really good approximation for the complex polarizability only for the small collision model considered by Gross and Sack. In the case of large inertial parameter values it is emphasized by means of plots of the complex polarizability that such an approximation always exhibits behavior characteristic of a sharply resonant system, i.e., a pronounced absorption peak well in excess of the Debye peak and a strongly negative real part, while the exact complex polarizability spectrum for the same parameter values merely displays inertia corrected Debye relaxation. Therefore, an explanation of the resonant term other than that based on a Rocard equation with a large inertial parameter must be sought as that equation strictly applies to inertia corrected Debye (heavily damped) relaxation only. The application of the itinerant oscillator model and the three variable Mori theory to the problem is discussed in view of this conclusion.
Steendam, R.; Van Steenbergen, M.J.; Hennink, W.E.; Frijlink, H.W.; Lerk, C.F.
2001-01-01
Different molecular weight grades of poly(DL-lactic acid) were applied as release controlling excipients in tablets for oral drug administration. The role of molecular weight and glass transition in the mechanism of water-induced volume expansion and drug release of PDLA tablets was investigated. Mo
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Das, Anuradha; Das, Suman; Biswas, Ranjit, E-mail: ranjit@bose.res.in [Chemical, Biological and Macromolecular Sciences, S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata, West Bengal 700098 (India)
2015-01-21
Temperature dependent relaxation dynamics, particle motion characteristics, and heterogeneity aspects of deep eutectic solvents (DESs) made of acetamide (CH{sub 3}CONH{sub 2}) and urea (NH{sub 2}CONH{sub 2}) have been investigated by employing time-resolved fluorescence measurements and all-atom molecular dynamics simulations. Three different compositions (f) for the mixture [fCH{sub 3}CONH{sub 2} + (1 − f)NH{sub 2}CONH{sub 2}] have been studied in a temperature range of 328-353 K which is ∼120-145 K above the measured glass transition temperatures (∼207 K) of these DESs but much lower than the individual melting temperature of either of the constituents. Steady state fluorescence emission measurements using probe solutes with sharply different lifetimes do not indicate any dependence on excitation wavelength in these metastable molten systems. Time-resolved fluorescence anisotropy measurements reveal near-hydrodynamic coupling between medium viscosity and rotation of a dissolved dipolar solute. Stokes shift dynamics have been found to be too fast to be detected by the time-resolution (∼70 ps) employed, suggesting extremely rapid medium polarization relaxation. All-atom simulations reveal Gaussian distribution for particle displacements and van Hove correlations, and significant overlap between non-Gaussian (α{sub 2}) and new non-Gaussian (γ) heterogeneity parameters. In addition, no stretched exponential relaxations have been detected in the simulated wavenumber dependent acetamide dynamic structure factors. All these results are in sharp contrast to earlier observations for ionic deep eutectics with acetamide [Guchhait et al., J. Chem. Phys. 140, 104514 (2014)] and suggest a fundamental difference in interaction and dynamics between ionic and non-ionic deep eutectic solvent systems.
Directory of Open Access Journals (Sweden)
Brian W Kunkle
Full Text Available In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV, and 'key genes' within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated. These 10 genes were able to predict tumor status with 96-100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential 'hubs of activity'. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several 'key genes' may be required for the development of glioblastoma. Further studies are needed to validate these 'key genes' as useful tools for early detection and novel therapeutic options for these tumors.
Directory of Open Access Journals (Sweden)
Simon Boitard
2016-03-01
Full Text Available Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey, PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.
Alvarado Mora, Mónica Viviana; Romano, Camila Malta; Gomes-Gouvêa, Michele Soares; Gutierrez, Maria Fernanda; Botelho, Livia; Carrilho, Flair José; Pinho, João Renato Rebello
2011-01-01
Hepatitis B is a worldwide health problem affecting about 2 billion people and more than 350 million are chronic carriers of the virus. Nine HBV genotypes (A to I) have been described. The geographical distribution of HBV genotypes is not completely understood due to the limited number of samples from some parts of the world. One such example is Colombia, in which few studies have described the HBV genotypes. In this study, we characterized HBV genotypes in 143 HBsAg-positive volunteer blood donors from Colombia. A fragment of 1306 bp partially comprising HBsAg and the DNA polymerase coding regions (S/POL) was amplified and sequenced. Bayesian phylogenetic analyses were conducted using the Markov Chain Monte Carlo (MCMC) approach to obtain the maximum clade credibility (MCC) tree using BEAST v.1.5.3. Of all samples, 68 were positive and 52 were successfully sequenced. Genotype F was the most prevalent in this population (77%) - subgenotypes F3 (75%) and F1b (2%). Genotype G (7.7%) and subgenotype A2 (15.3%) were also found. Genotype G sequence analysis suggests distinct introductions of this genotype in the country. Furthermore, we estimated the time of the most recent common ancestor (TMRCA) for each HBV/F subgenotype and also for Colombian F3 sequences using two different datasets: (i) 77 sequences comprising 1306 bp of S/POL region and (ii) 283 sequences comprising 681 bp of S/POL region. We also used two other previously estimated evolutionary rates: (i) 2.60 × 10(-4)s/s/y and (ii) 1.5 × 10(-5)s/s/y. Here we report the HBV genotypes circulating in Colombia and estimated the TMRCA for the four different subgenotypes of genotype F.
Kunkle, Brian W; Yoo, Changwon; Roy, Deodutta
2013-01-01
In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and 'key genes' within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96-100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential 'hubs of activity'. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several 'key genes' may be required for the development of glioblastoma. Further studies are needed to validate these 'key genes' as useful tools for early detection and novel therapeutic options for these tumors.
Sorokin, S. V.; Klimko, G. V.; Sedova, I. V.; Sitnikova, A. A.; Kirilenko, D. A.; Baidakova, M. V.; Yagovkina, M. A.; Komissarova, T. A.; Belyaev, K. G.; Ivanov, S. V.
2016-12-01
This paper presents a comprehensive study of structural, optical and electrical properties of heterostructures with linearly graded InxGa1-xAs metamorphic buffer layers (MBLs) grown by molecular beam epitaxy on GaAs (001) substrates. The low density of threading dislocations (well below 106 cm-2) in 1-μm-thick In0.3Ga0.7As layers grown atop of the linearly graded InxGa1-xAs/GaAs MBLs has been confirmed by using transmission electron microscopy (TEM). X-ray diffraction (XRD) data demonstrate good agreement between the experimentally measured In step-back and its calculations in the frames of existing models. Combining the XRD reciprocal space maps (RSM) of the structures and the spatially-resolved selective area electron diffraction measurements by cross-sectional TEM in depth-profiled RSM diagrams allowed direct visualization of the strain relaxation dynamics during the MBL growth. Strong effect of the azimuth angle and the value of an unintentional initial miscut of nominally (001) oriented GaAs substrate on the strain relaxation dynamics was observed.
Structure learning for Bayesian networks as models of biological networks.
Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri
2013-01-01
Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.
Fleischmann, C.; Lieten, R. R.; Hermann, P.; Hönicke, P.; Beckhoff, B.; Seidel, F.; Richard, O.; Bender, H.; Shimura, Y.; Zaima, S.; Uchida, N.; Temst, K.; Vandervorst, W.; Vantomme, A.
2016-08-01
Strained Ge1-xSnx thin films have recently attracted a lot of attention as promising high mobility or light emitting materials for future micro- and optoelectronic devices. While they can be grown nowadays with high crystal quality, the mechanism by which strain energy is relieved upon thermal treatments remains speculative. To this end, we investigated the evolution (and the interplay) of composition, strain, and morphology of strained Ge0.94Sn0.06 films with temperature. We observed a diffusion-driven formation of Sn-enriched islands (and their self-organization) as well as surface depressions (pits), resulting in phase separation and (local) reduction in strain energy, respectively. Remarkably, these compositional and morphological instabilities were found to be the dominating mechanisms to relieve energy, implying that the relaxation via misfit generation and propagation is not intrinsic to compressively strained Ge0.94Sn0.06 films grown by molecular beam epitaxy.
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
Directory of Open Access Journals (Sweden)
Sérgio L. Pereira
2008-01-01
Full Text Available Most Neotropical birds, including Pteroglossus aracaris, do not have an adequate fossil record to be used as time constraints in molecular dating. Hence, the evolutionary timeframe of the avian biota can only be inferred using alternative time constraints. We applied a Bayesian relaxed clock approach to propose an alternative interpretation for the historical biogeography of Pteroglossus based on mitochondrial DNA sequences, using different combinations of outgroups and time constraints obtained from outgroup fossils, vicariant barriers and molecular time estimates. The results indicated that outgroup choice has little effect on the Bayesian posterior distribution of divergence times within Pteroglossus , that geological and molecular time constraints seem equally suitable to estimate the Bayesian posterior distribution of divergence times for Pteroglossus , and that the fossil record alone overestimates divergence times within the fossil-lacking ingroup. The Bayesian estimates of divergence times suggest that the radiation of Pteroglossus occurred from the Late Miocene to the Pliocene (three times older than estimated by the “standard” mitochondrial rate of 2% sequence divergence per million years, likely triggered by Andean uplift, multiple episodes of marine transgressions in South America, and formation of present-day river basins. The time estimates are in agreement with other Neotropical taxa with similar geographic distributions.
Janzen, Thijs; Hoehna, Sebastian; Etienne, Rampal S.
2015-01-01
Molecular phylogenies form a potential source of information on rates of diversification, and the mechanisms that underlie diversification patterns. Diversification models have become increasingly complex over the past decade, and we have reached a point where the computation of the analytical likel
Zhang, Rui; He, Qinghao; Chatfield, David; Wang, Xiaotang
2013-05-28
To unravel the mechanism of chloroperoxidase (CPO)-catalyzed regioselective oxidation of indole, we studied the structure of the CPO-indole complex using nuclear magnetic resonance (NMR) relaxation measurements and computational techniques. The dissociation constant (KD) of the CPO-indole complex was calculated to be approximately 21 mM. The distances (r) between protons of indole and the heme iron calculated via NMR relaxation measurements and molecular docking revealed that the pyrrole ring of indole is oriented toward the heme with its 2-H pointing directly at the heme iron. Both KD and r values are independent of pH in the range of 3.0-6.5. The stability and structure of the CPO-indole complex are also independent of the concentration of chloride or iodide ion. Molecular docking suggests the formation of a hydrogen bond between the NH group of indole and the carboxyl O of Glu 183 in the binding of indole to CPO. Simulated annealing of the CPO-indole complex using r values from NMR experiments as distance restraints reveals that the van der Waals interactions were much stronger than the Coulomb interactions in the binding of indole to CPO, indicating that the association of indole with CPO is primarily governed by hydrophobic rather than electrostatic interactions. This work provides the first experimental and theoretical evidence of the long-sought mechanism that leads to the "unexpected" regioselectivity of the CPO-catalyzed oxidation of indole. The structure of the CPO-indole complex will serve as a lighthouse in guiding the design of CPO mutants with tailor-made activities for biotechnological applications.
De Nicola, Antonio; Kawakatsu, Toshihiro; Milano, Giuseppe
2014-12-09
A procedure based on Molecular Dynamics (MD) simulations employing soft potentials derived from self-consistent field (SCF) theory (named MD-SCF) able to generate well-relaxed all-atom structures of polymer melts is proposed. All-atom structures having structural correlations indistinguishable from ones obtained by long MD relaxations have been obtained for poly(methyl methacrylate) (PMMA) and poly(ethylene oxide) (PEO) melts. The proposed procedure leads to computational costs mainly related on system size rather than to the chain length. Several advantages of the proposed procedure over current coarse-graining/reverse mapping strategies are apparent. No parametrization is needed to generate relaxed structures of different polymers at different scales or resolutions. There is no need for special algorithms or back-mapping schemes to change the resolution of the models. This characteristic makes the procedure general and its extension to other polymer architectures straightforward. A similar procedure can be easily extended to the generation of all-atom structures of block copolymer melts and polymer nanocomposites.
Murasawa, Shinpei; Iuchi, Katsuya; Sato, Shinichi; Noguchi-Yachide, Tomomi; Sodeoka, Mikiko; Yokomatsu, Tsutomu; Dodo, Kosuke; Hashimoto, Yuichi; Aoyama, Hiroshi
2012-11-01
A structure consisting of substituted hydantoin linked to a 5-(halophenyl)furan-2-yl group via an amide bond was identified as a promising scaffold for development of low-molecular-weight therapeutic agents to treat vascular dysfunction, including ischemia/reperfusion injury. Among the compounds synthesized, 5-(3,5-dichlorophenyl)-N-{2,4-dioxo-3-[(pyridin-3-yl)methyl]imidazolidin-1-yl}-2-furamide (17) possessed the most potent inhibitory activity against Ca(2+)-induced mitochondrial swelling. The structural development, synthesis and structure-activity relationship of these compounds are described.
Introduction to Bayesian statistics
Bolstad, William M
2017-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
Hanford, Amanda D; O'Connor, Patrick D; Anderson, James B; Long, Lyle N
2008-06-01
In the current study, real gas effects in the propagation of sound waves are simulated using the direct simulation Monte Carlo method for a wide range of frequencies. This particle method allows for treatment of acoustic phenomena at high Knudsen numbers, corresponding to low densities and a high ratio of the molecular mean free path to wavelength. Different methods to model the internal degrees of freedom of diatomic molecules and the exchange of translational, rotational and vibrational energies in collisions are employed in the current simulations of a diatomic gas. One of these methods is the fully classical rigid-rotor/harmonic-oscillator model for rotation and vibration. A second method takes into account the discrete quantum energy levels for vibration with the closely spaced rotational levels classically treated. This method gives a more realistic representation of the internal structure of diatomic and polyatomic molecules. Applications of these methods are investigated in diatomic nitrogen gas in order to study the propagation of sound and its attenuation and dispersion along with their dependence on temperature. With the direct simulation method, significant deviations from continuum predictions are also observed for high Knudsen number flows.
Bayesian artificial intelligence
Korb, Kevin B
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
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
2016-01-01
Over recent years, several alternative relaxed clock models have been proposed in the context of Bayesian dating. These models fall in two distinct categories: uncorrelated and autocorrelated across branches. The choice between these two classes of relaxed clocks is still an open question. More fundamentally, the true process of rate variation may have both long-term trends and short-term fluctuations, suggesting that more sophisticated clock models unfolding over multiple time scales should ultimately be developed. Here, a mixed relaxed clock model is introduced, which can be mechanistically interpreted as a rate variation process undergoing short-term fluctuations on the top of Brownian long-term trends. Statistically, this mixed clock represents an alternative solution to the problem of choosing between autocorrelated and uncorrelated relaxed clocks, by proposing instead to combine their respective merits. Fitting this model on a dataset of 105 placental mammals, using both node-dating and tip-dating approaches, suggests that the two pure clocks, Brownian and white noise, are rejected in favour of a mixed model with approximately equal contributions for its uncorrelated and autocorrelated components. The tip-dating analysis is particularly sensitive to the choice of the relaxed clock model. In this context, the classical pure Brownian relaxed clock appears to be overly rigid, leading to biases in divergence time estimation. By contrast, the use of a mixed clock leads to more recent and more reasonable estimates for the crown ages of placental orders and superorders. Altogether, the mixed clock introduced here represents a first step towards empirically more adequate models of the patterns of rate variation across phylogenetic trees. This article is part of the themed issue ‘Dating species divergences using rocks and clocks’. PMID:27325829
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Koyama, T.; Onuma, T.; Chichibu, S.F. [Institute of Applied Physics and Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8573 (Japan); NICP, ERATO, Japan Science and Technology Agency (JST), Kawaguchi 332-0012 (Japan); Sugawara, M.; Uchinuma, Y. [Institute of Applied Physics and Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8573 (Japan); Kaeding, J.F.; Sharma, R. [Department of Materials Engineering, University of California, Santa Barbara, CA 93106 (United States); Nakamura, S. [NICP, ERATO, Japan Science and Technology Agency (JST), Kawaguchi 332-0012 (Japan); Department of Materials Engineering, University of California, Santa Barbara, CA 93106 (United States)
2006-05-15
Temporal evolution of surface morphology in AlN epilayers grown by NH{sub 3}-source molecular beam epitaxy on the GaN/(0001) Al{sub 2}O{sub 3} epitaxial templates was correlated with changes in the degree of the residual strain and the layer thickness. They began to crack for the thickness as thin as 10 nm. However, atomic-layer step-and-terrace surface structures were maintained for the thickness up to 32 nm. Tensile biaxial stress decreased with further increase in the thickness due to the lattice relaxation, which caused surface roughening. An 1580-nm-thick, nearly strain-compensated AlN epilayer, of which threading dislocation density was reduced down to 6 x 10{sup 9} cm{sup -2}, exhibited excitonic photoluminescence peaks at 6.002 and 6.023 eV at 9 K and a near-band-edge peak at 5.872 eV at 293 K. (copyright 2006 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Marzola, Luca; Raidal, Martti
2016-11-01
Motivated by natural inflation, we propose a relaxation mechanism consistent with inflationary cosmology that explains the hierarchy between the electroweak scale and Planck scale. This scenario is based on a selection mechanism that identifies the low-scale dynamics as the one that is screened from UV physics. The scenario also predicts the near-criticality and metastability of the Standard Model (SM) vacuum state, explaining the Higgs boson mass observed at the Large Hadron Collider (LHC). Once Majorana right-handed neutrinos are introduced to provide a viable reheating channel, our framework yields a corresponding mass scale that allows for the seesaw mechanism as well as for standard thermal leptogenesis. We argue that considering singlet scalar dark matter extensions of the proposed scenario could solve the vacuum stability problem and discuss how the cosmological constant problem is possibly addressed.
Applied Bayesian Hierarchical Methods
Congdon, Peter D
2010-01-01
Bayesian methods facilitate the analysis of complex models and data structures. Emphasizing data applications, alternative modeling specifications, and computer implementation, this book provides a practical overview of methods for Bayesian analysis of hierarchical models.
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
Strugnell, Jan; Norman, Mark; Jackson, Jennifer; Drummond, Alexei J; Cooper, Alan
2005-11-01
The resolution of higher level phylogeny of the coleoid cephalopods (octopuses, squids, and cuttlefishes) has been hindered by homoplasy among morphological characters in conjunction with a very poor fossil record. Initial molecular studies, based primarily on small fragments of single mitochondrial genes, have produced little resolution of the deep relationships amongst coleoid cephalopod families. The present study investigated this issue using 3415 base pairs (bp) from three nuclear genes (octopine dehydrogenase, pax-6, and rhodopsin) and three mitochondrial genes (12S rDNA, 16S rDNA, and cytochrome oxidase I) from a total of 35 species (including representatives of each of the higher level taxa). Bayesian analyses were conducted on mitochondrial and nuclear genes separately and also all six genes together. Separate analyses were conducted with the data partitioned by gene, codon/rDNA, gene+codon/rDNA or not partitioned at all. In the majority of analyses partitioning the data by gene+codon was the appropriate model with partitioning by codon the second most selected model. In some instances the topology varied according to the model used. Relatively high posterior probabilities and high levels of congruence were present between the topologies resulting from the analysis of all Octopodiform (octopuses and vampire "squid") taxa for all six genes, and independently for the datasets of mitochondrial and nuclear genes. In contrast, the highest levels of resolution within the Decapodiformes (squids and cuttlefishes) resulted from analysis of nuclear genes alone. Different higher level Decapodiform topologies were obtained through the analysis of only the 1st+2nd codon positions of nuclear genes and of all three codon positions. It is notable that there is strong evidence of saturation among the 3rd codon positions within the Decapodiformes and this may contribute spurious signal. The results suggest that the Decapodiformes may have radiated earlier and/or had faster
Reale, Riccardo; English, Niall J; Garate, José-Antonio; Marracino, Paolo; Liberti, Micaela; Apollonio, Francesca
2013-11-28
Water self-diffusion and the dipolar response of the selectivity filter within human aquaporin 4 have been studied using molecular dynamics (MD) simulations in the absence and presence of pulses of external static and alternating electric fields. The pulses were approximately 50 and 100 ns in duration and 0.0065 V/Å in (r.m.s.) intensity and were either static or else 2.45 or 100 GHz in frequency and applied both along and perpendicular to the channels. In addition, the relaxation of the aquaporin, water self-diffusion and gating dynamics following cessation of the impulses was studied. In previous work it was determined that switches in the dihedral angle of the selectivity filter led to boosting of water permeation events within the channels, in the presence of identical external static and alternating electric fields, although applied continuously. Here the application of field impulses (and subsequently, upon removal) has shown that it is the dipolar orientation of the histidine-201 residue in the selectivity filter which governs the dihedral angle, and hence influences water self-diffusion; this constitutes an appropriate order parameter. The dipolar response of this residue to the applied field leads to the adoption of four distinct states, which we modelled as time-homogeneous Markov jump processes, and may be distinguished in the potential of mean force (PMF) as a function of the dipolar orientation of histidine-201. The observations of enhanced "dipolar flipping" of H201 serve to explain increased levels of water self-diffusion within aquaporin channels during, and immediately following, field impulses, although the level of statistical certainty here is lower. Given the appreciable size of the energy barriers evident in PMFs computed directly from deterministic MD (whether in the absence or presence of external fields), metadynamics calculations were undertaken to explore the free-energy landscape of histidine-201 orientation with greater accuracy and
Bayesian Games with Intentions
Directory of Open Access Journals (Sweden)
Adam Bjorndahl
2016-06-01
Full Text Available 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.
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
Grzegorczyk, Marco; Husmeier, Dirk
2013-01-01
To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying assumption is that the parameters associated with time series segments delimited by multiple changepoints are a priori inde
Directory of Open Access Journals (Sweden)
Andrea Messori
2014-09-01
Full Text Available Background: In major orthopedic surgery, prevention of venous thromboembolism has been based on Unfractionated Heparins (UFHs over the past decades, then on Low-Molecular Weight Heparins (LMWHs, and on New Oral Anticoagulants (NOACs more recently. To summarize the comparative effectiveness of UFHs, LMWHs, and NOACs in this clinical indication, we applied Bayesian network meta-analysis to the clinical material (randomized studies published in two previous reviews focused on this issue.. Objectives: Our end-point was a composite of venous thromboembolism and pulmonary embolism.. Materials and Methods: Our analysis was based on standard Bayesian network meta-analysis (random-effect model.. Results: The analysis included 21 randomized trials for a total of 21,805 patients. Our results showed that the degree of effectiveness did not differ among UFHs, LMWHs, and NOACs. Although some trends emerged from an in-depth analysis of these data (e.g. according to the histogram of rankings, no significant differences were found (P > 0.05. Moreover, two agents among LMWHs proved to be adequately supported by randomized trials (enoxaparin and dalteparin, while limited evidence was available for other agents of this class.. Conclusions: Our synthesis of the effectiveness data can be useful as an overall reference in this area and can also contribute to defining the place of further innovative treatments for this clinical indication..
Directory of Open Access Journals (Sweden)
Nima Kasraie
2011-01-01
Full Text Available The aims of this study were to determine whether standard extracellular contrast agents of Gd(III ions in combination with a polymeric entity susceptible to hydrolytic degradation over a finite period of time, such as Hyaluronic Acid (HA, have sufficient vascular residence time to obtain comparable vascular imaging to current conventional compounds and to obtain sufficient data to show proof of concept that HA with Gd-DTPA ligands could be useful as vascular imaging agents. We assessed the dynamic relaxivity of the HA bound DTPA compounds using a custom-made phantom, as well as relaxation rates at 10.72 MHz with concentrations ranging between 0.09 and 7.96 mM in phosphate-buffered saline. Linear dependences of static longitudinal relaxation rate (R1 on concentration were found for most measured samples, and the HA samples continued to produce high signal strength after 24 hours after injection into a dialysis cassette at 3T, showing superior dynamic relaxivity values compared to conventional contrast media such as Gd-DTPA-BMA.
Thermal relaxation and mechanical relaxation of rice gel
Institute of Scientific and Technical Information of China (English)
丁玉琴; 赵思明; 熊善柏
2008-01-01
Rice gel was prepared by simulating the production processes of Chinese local rice noodles,and the properties of thermal relaxation and mechanical relaxation during gelatinization were studied by differential scanning calorimetry(DSC) measurement and dynamic rheometer.The results show that during gelatinization,the molecular chains of rice starch undergo the thermal relaxation and mechanical relaxation.During the first heating and high temperature holding processes,the starch crystallites in the rice slurry melt,and the polymer chains stretch and interact,then viscoelastic gel forms.The cooling and low temperatures holding processes result in reinforced networks and decrease the viscoelasticity of the gel.During the second heating,the remaining starch crystallites further melt,the network is reinforced,and the viscoelasticity increases.The viscoelasticity,the molecular conformation and texture of the gel are adjusted by changing the temperature,and finally construct the gel with the textural characteristics of Chinese local rice noodle.
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…
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
Bayesian modelling of geostatistical malaria risk data
Directory of Open Access Journals (Sweden)
L. Gosoniu
2006-11-01
Full Text Available Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.
Bayesian modelling of geostatistical malaria risk data.
Gosoniu, L; Vounatsou, P; Sogoba, N; Smith, T
2006-11-01
Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.
Konstruksi Bayesian Network Dengan Algoritma Bayesian Association Rule Mining Network
Octavian
2015-01-01
Beberapa tahun terakhir, Bayesian Network telah menjadi konsep yang populer digunakan dalam berbagai bidang kehidupan seperti dalam pengambilan sebuah keputusan dan menentukan peluang suatu kejadian dapat terjadi. Sayangnya, pengkonstruksian struktur dari Bayesian Network itu sendiri bukanlah hal yang sederhana. Oleh sebab itu, penelitian ini mencoba memperkenalkan algoritma Bayesian Association Rule Mining Network untuk memudahkan kita dalam mengkonstruksi Bayesian Network berdasarkan data ...
Baldo, M.; Grassi, A.; Guidoni, L.; Nicolini, M.; Pappalardo, G. C.; Viti, V.
The spin-lattice relaxation times ( T1) of carbon-13 resonances of the drug 2-oxopyrrolidin- 1-ylacetamide ( 2OPYAC) were determined in CDCl 3 + DMSO and H 2O solutions to investigate the internal conformational flexibility. The measured T1s for the hydrogen-bearing carbon atoms of the 2-pyrrolidone ring fragment were diagnostic of a rigid conformation with respect to the acetamide linked moiety. The model of anisotropic reorientation of a rigid body was used to analyse the measured relaxation data in terms of a single conformation. Owing to the small number of T1 data available the fitting procedure for each of the possible conformations failed. The structure corresponding to the rigid conformation was therefore considered to be the one that is strongly stabilized by internal hydrogen bonding as predicted on the basis of theoretical MO ab initio quantum chemical calculations.
Pietras, Rafał; Sarewicz, Marcin; Osyczka, Artur
2014-01-01
Measurements of specific interactions between proteins are challenging. In redox systems, interactions involve surfaces near the attachment sites of cofactors engaged in interprotein electron transfer (ET). Here we analyzed binding of cytochrome c 2 to cytochrome bc 1 by measuring paramagnetic relaxation enhancement (PRE) of spin label (SL) attached to cytochrome c 2. PRE was exclusively induced by the iron atom of heme c 1 of cytochrome bc 1, which guaranteed that only the configurations wit...
Structure-based bayesian sparse reconstruction
Quadeer, Ahmed Abdul
2012-12-01
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
Model Diagnostics for Bayesian Networks
Sinharay, Sandip
2006-01-01
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Kobayashi, M; Irisawa, H
1961-10-27
The latent period of relaxation of molluscan myocardium due to anodal current is much longer than that of contraction. Although the rate and the grade of relaxation are intimately related to both the stimulus condition and the muscle tension, the latent period of relaxation remains constant, except when the temperature of the bathing fluid is changed.
Vibrational and Rotational Energy Relaxation in Liquids
DEFF Research Database (Denmark)
Petersen, Jakob
Vibrational and rotational energy relaxation in liquids are studied by means of computer simulations. As a precursor for studying vibrational energy relaxation of a solute molecule subsequent to the formation of a chemical bond, the validity of the classical Bersohn-Zewail model for describing......, the vibrational energy relaxation of I2 subsequent to photodissociation and recombination in CCl4 is studied using classical Molecular Dynamics simulations. The vibrational relaxation times and the time-dependent I-I pair distribution function are compared to new experimental results, and a qualitative agreement...... is found in both cases. Furthermore, the rotational energy relaxation of H2O in liquid water is studied via simulations and a power-and-work analysis. The mechanism of the energy transfer from the rotationally excited H2O molecule to its water neighbors is elucidated, i.e. the energy-accepting degrees...
Institute of Scientific and Technical Information of China (English)
LI ZhiQiang; GUO BaoCheng; LI JunBing; HE ShunPing; CHEN YiYu
2008-01-01
The genus Sinocyclocheilus is distributed in Yun-Gui Plateau and its surrounding region only, within more than 10 cave species showing different degrees of degeneration of eyes and pigmentation with wonderful adaptations. To present, published morphological and molecular phylogenetic hypotheses of Sinocyclocheilus from prior works are very different and the relationships within the genus are still far from clear. We obtained the sequences of cytochrome b (cyt b) and NADH dehydrogenase subunit 4 (ND4) of 34 species within Sinocyclocheilus, which represent the most dense taxon sampling to date. We performed Bayesian mixed models analyses with this data set. Under this phylogenetic framework, we estimated the divergence times of recovered clades using different methods under relaxed molecular clock. Our phyloegentic results supported the monophyly of Sinocyclocheilus and showed that this genus could be subdivided into 6 major clades. In addition, an earlier finding demonstrating the polyphyletic of cave species and the most basal position of S. jii was corroborated. Relaxed divergence-time estimation suggested that Sinocyclocheilus originated at the late Miocene, about 11 million years ago (Ma), which is older than what have been assumed.
Bayesian Lensing Shear Measurement
Bernstein, Gary M
2013-01-01
We derive an estimator of weak gravitational lensing shear from background galaxy images that avoids noise-induced biases through a rigorous Bayesian treatment of the measurement. The Bayesian formalism requires a prior describing the (noiseless) distribution of the target galaxy population over some parameter space; this prior can be constructed from low-noise images of a subsample of the target population, attainable from long integrations of a fraction of the survey field. We find two ways to combine this exact treatment of noise with rigorous treatment of the effects of the instrumental point-spread function and sampling. The Bayesian model fitting (BMF) method assigns a likelihood of the pixel data to galaxy models (e.g. Sersic ellipses), and requires the unlensed distribution of galaxies over the model parameters as a prior. The Bayesian Fourier domain (BFD) method compresses galaxies to a small set of weighted moments calculated after PSF correction in Fourier space. It requires the unlensed distributi...
Fox, G.J.A.; Berg, van den S.M.; Veldkamp, B.P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item resp
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...
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.
1H relaxation dispersion in solutions of nitroxide radicals: Influence of electron spin relaxation
Kruk, D.; Korpała, A.; Kubica, A.; Kowalewski, J.; Rössler, E. A.; Moscicki, J.
2013-03-01
The work presents a theory of nuclear (1H) spin-lattice relaxation dispersion for solutions of 15N and 14N radicals, including electron spin relaxation effects. The theory is a generalization of the approach presented by Kruk et al. [J. Chem. Phys. 137, 044512 (2012)], 10.1063/1.4736854. The electron spin relaxation is attributed to the anisotropic part of the electron spin-nitrogen spin hyperfine interaction modulated by rotational dynamics of the paramagnetic molecule, and described by means of Redfield relaxation theory. The 1H relaxation is caused by electron spin-proton spin dipole-dipole interactions which are modulated by relative translational motion of the solvent and solute molecules. The spectral density characterizing the translational dynamics is described by the force-free-hard-sphere model. The electronic relaxation influences the 1H relaxation by contributing to the fluctuations of the inter-molecular dipolar interactions. The developed theory is tested against 1H spin-lattice relaxation dispersion data for glycerol solutions of 4-oxo-TEMPO-d16-15N and 4-oxo-TEMPO-d16-14N covering the frequency range of 10 kHz-20 MHz. The studies are carried out as a function of temperature starting at 328 K and going down to 290 K. The theory gives a consistent overall interpretation of the experimental data for both 14N and 15N systems and explains the features of 1H relaxation dispersion resulting from the electron spin relaxation.
Stress relaxation of bi-disperse polystyrene melts
DEFF Research Database (Denmark)
Hengeller, Ludovica; Huang, Qian; Dorokhin, Andriy
2016-01-01
We present start-up of uniaxial extension followed by stress relaxation experiments of a bi-disperse 50 % by weight blend of 95k and 545k molecular weight polystyrene. We also show, for comparison, stress relaxation measurements of the polystyrene melts with molecular weight 95k and 545k, which a...
Protein dynamics from nuclear magnetic relaxation.
Charlier, Cyril; Cousin, Samuel F; Ferrage, Fabien
2016-05-01
Nuclear magnetic resonance is a ubiquitous spectroscopic tool to explore molecules with atomic resolution. Nuclear magnetic relaxation is intimately connected to molecular motions. Many methods and models have been developed to measure and interpret the characteristic rates of nuclear magnetic relaxation in proteins. These approaches shed light on a rich and diverse range of motions covering timescales from picoseconds to seconds. Here, we introduce some of the basic concepts upon which these approaches are built and provide a series of illustrations.
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.
Institute of Scientific and Technical Information of China (English)
Toshiro SHIBANO; Paul M VANHOUTTE
2003-01-01
AIM: To determine whether or not low molecular G-proteins are involved in the endothelium-dependent relaxations to bradykinin. METHODS: The effects of botulinum ADP-ribosyltranferase C3 were studied in porcine coronary arteries and endothelial cells. RESULTS: Incubation of membrane fractions isolated from endothelial cells with the enzyme and 32p-NAD resulted in the ribosylation of the proteins with molecular weight of 24-25 kDa. Radio labelling of these proteins was suppressed in the presence of guanosine 5t-O-(3-thiotriphosphate) (GTP-yS), a hydrolysis-resistant analog of GTP. In the isolated arteries, ADP-ribosyltransferase C3 attenuated the relaxations tobradykinin during contractions with prostaglandin F2α in the presence of tween 80 (non ionic detergent), but not in the absence of tween 80. CONCLUSION: Low molecular weight G-proteins of the Rho family contribute to the mechanism of relaxation induced by bradykinin.
Bayesian Face Sketch Synthesis.
Wang, Nannan; Gao, Xinbo; Sun, Leiyu; Li, Jie
2017-03-01
Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.
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.
Hybrid Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2012-01-01
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm t...
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.
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 corr......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...
Center, Julian L.; Knuth, Kevin H.
2011-03-01
Visual odometry refers to tracking the motion of a body using an onboard vision system. Practical visual odometry systems combine the complementary accuracy characteristics of vision and inertial measurement units. The Mars Exploration Rovers, Spirit and Opportunity, used this type of visual odometry. The visual odometry algorithms in Spirit and Opportunity were based on Bayesian methods, but a number of simplifying approximations were needed to deal with onboard computer limitations. Furthermore, the allowable motion of the rover had to be severely limited so that computations could keep up. Recent advances in computer technology make it feasible to implement a fully Bayesian approach to visual odometry. This approach combines dense stereo vision, dense optical flow, and inertial measurements. As with all true Bayesian methods, it also determines error bars for all estimates. This approach also offers the possibility of using Micro-Electro Mechanical Systems (MEMS) inertial components, which are more economical, weigh less, and consume less power than conventional inertial components.
Probabilistic Inferences in Bayesian Networks
Ding, Jianguo
2010-01-01
This chapter summarizes the popular inferences methods in Bayesian networks. The results demonstrates that the evidence can propagated across the Bayesian networks by any links, whatever it is forward or backward or intercausal style. The belief updating of Bayesian networks can be obtained by various available inference techniques. Theoretically, exact inferences in Bayesian networks is feasible and manageable. However, the computing and inference is NP-hard. That means, in applications, in ...
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
Pietras, Rafał; Sarewicz, Marcin; Osyczka, Artur
2014-06-19
Measurements of specific interactions between proteins are challenging. In redox systems, interactions involve surfaces near the attachment sites of cofactors engaged in interprotein electron transfer (ET). Here we analyzed binding of cytochrome c2 to cytochrome bc1 by measuring paramagnetic relaxation enhancement (PRE) of spin label (SL) attached to cytochrome c2. PRE was exclusively induced by the iron atom of heme c1 of cytochrome bc1, which guaranteed that only the configurations with SL to heme c1 distances up to ∼30 Å were detected. Changes in PRE were used to qualitatively and quantitatively characterize the binding. Our data suggest that at low ionic strength and under an excess of cytochrome c2 over cytochrome bc1, several cytochrome c2 molecules gather near the binding domain forming a "cloud" of molecules. When the cytochrome bc1 concentration increases, the cloud disperses to populate additional available binding domains. An increase in ionic strength weakens the attractive forces and the average distance between cytochrome c2 and cytochrome bc1 increases. The spatial arrangement of the protein complex at various ionic strengths is different. Above 150 mM NaCl the lifetime of the complexes becomes so short that they are undetectable. All together the results indicate that cytochrome c2 molecules, over the range of salt concentration encompassing physiological ionic strength, do not form stable, long-lived complexes but rather constantly collide with the surface of cytochrome bc1 and ET takes place coincidentally with one of these collisions.
Energy Technology Data Exchange (ETDEWEB)
Rumpel, Sigrun; Becker, Stefan; Zweckstetter, Markus [Max Planck Institute for Biophysical Chemistry, Department for NMR-based Structural Biology (Germany)], E-mail: mzwecks@gwdg.de
2008-01-15
Structure determination of homooligomeric proteins by NMR spectroscopy is difficult due to the lack of chemical shift perturbation data, which is very effective in restricting the binding interface in heterooligomeric systems, and the difficulty of obtaining a sufficient number of intermonomer distance restraints. Here we solved the high-resolution solution structure of the 15.4 kDa homodimer CylR2, the regulator of cytolysin production from Enterococcus faecalis, which deviates by 1.1 A from the previously determined X-ray structure. We studied the influence of different experimental information such as long-range distances derived from paramagnetic relaxation enhancement, residual dipolar couplings, symmetry restraints and intermonomer Nuclear Overhauser Effect restraints on the accuracy of the derived structure. In addition, we show that it is useful to combine experimental information with methods of ab initio docking when the available experimental data are not sufficient to obtain convergence to the correct homodimeric structure. In particular, intermonomer distances may not be required when residual dipolar couplings are compared to values predicted on the basis of the charge distribution and the shape of ab initio docking solutions.
Vibrational energy relaxation in liquid oxygen
Everitt, K. F.; Egorov, S. A.; Skinner, J. L.
1998-09-01
We consider theoretically the relaxation from the first excited vibrational state to the ground state of oxygen molecules in neat liquid oxygen. The relaxation rate constant is related in the usual way to the Fourier transform of a certain quantum mechanical force-force time-correlation function. A result from Egelstaff allows one instead to relate the rate constant (approximately) to the Fourier transform of a classical force-force time-correlation function. This Fourier transform is then evaluated approximately by calculating three equilibrium averages from a classical molecular dynamics simulation. Our results for the relaxation times (at two different temperatures) are within a factor of 5 of the experimental relaxation times, which are in the ms range.
Energy Technology Data Exchange (ETDEWEB)
Iuchi, Satoru; Koga, Nobuaki [Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601 (Japan)
2015-12-31
A model electronic Hamiltonian of [Fe(bpy){sub 3}]{sup 2+}, which was recently refined for use in molecular dynamics simulations, is reviewed with some additional results. In particular, the quality of the refined model Hamiltonian is examined in terms of the vibrational frequencies and solvation structures of the lowest singlet and quintet states.
Bayesian methods for hackers probabilistic programming and Bayesian inference
Davidson-Pilon, Cameron
2016-01-01
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...
Bayesian networks in neuroscience: a survey.
Bielza, Concha; Larrañaga, Pedro
2014-01-01
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes A and B indicates a direct influence between the state of A and the state of B, which in some domains can also be interpreted as a causal relation. The wide-spread use of Bayesian networks...
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 experimental...
实时脉冲光声法用于分子弛豫时间的测定%Real-time pulsed photoacoustics-molecular relaxation time measurements
Institute of Scientific and Technical Information of China (English)
Markushev Dragan; Rabasovic Mihailo; Lukic Mladena; Cojbasic Zarko; Todorovic Dragan
2013-01-01
脉冲光声光谱法一个重要的应用是确定气体分子的振动—平动弛豫时间τV-T.因激光光束的空间分布R(r)对光声测量有显著影响,我们发展了同时测定R(r)和τV-T的方法.本方法基于光声脉冲的实时信号和一种用于光声成像的数学运算法则.本文讨论了智能计算用于多原子气体分子的R(r)和τV-T同时和实时测定的可能性.进一步利用前馈多层神经网络的离线批训练法,结合一个理论光声信号对R(r)和τV-T进行了同时和实时分析.本方法可明显缩短确定上述参数所需时间.%Determination of the vibrational-to-translational relaxation time rv-T in gases is one of the applications of pulsed photoacoustic spectroscopy.Because the spatial profile of the laser beam R (r)can significantly influence the accuracy of the photoacoustic measurements,we developed the method for simultaneous determination of the R(r)and τv T.It is based on the temporal shape of the photoacoustic pulse and utilizes a mathematical algorithm developed for photoacoustic tomography.The possibilities of computational intelligence application for simultaneous and real-time determination of R(r)and rv-T values of polyatomic molecules in gases by pulsed photoacoustic are also discussed.Feed forward multilayer perception networks are trained in an offline batch training regime to estimate simultaneously,and in real-time,R(r) (profile shape class) and τv T from a given (theoretical) photoacoustic signals.Proposed method significantly shortens the time required for the simultaneous determination of the afore mentioned quantities.
Indentation load relaxation test
Energy Technology Data Exchange (ETDEWEB)
Hannula, S.P.; Stone, D.; Li, C.Y. (Cornell Univ., Ithaca, NY (USA))
Most of the models that are used to describe the nonelastic behavior of materials utilize stress-strain rate relations which can be obtained by a load relaxation test. The conventional load relaxation test, however, cannot be performed if the volume of the material to be tested is very small. For such applications the indentation type of test offers an attractive means of obtaining data necessary for materials characterization. In this work the feasibility of the indentation load relaxation test is studied. Experimental techniques are described together with results on Al, Cu and 316 SS. These results are compared to those of conventional uniaxial load relaxation tests, and the conversion of the load-indentation rate data into the stress-strain rate data is discussed.
Relaxation techniques for stress
... problems such as high blood pressure, stomachaches, headaches, anxiety, and depression. Using relaxation techniques can help you feel calm. These exercises can also help you manage stress and ease the effects of stress on your body.
Beckmann, Peter A; Rheingold, Arnold L
2016-04-21
The dynamics of methyl (CH3) and fluoromethyl (CF3) groups in organic molecular (van der Waals) solids can be exploited to survey their local environments. We report solid state (1)H and (19)F spin-lattice relaxationexperiments in polycrystalline 3-trifluoromethoxycinnamic acid, along with an X-ray diffraction determination of the molecular and crystal structure, to investigate the intramolecular and intermolecular interactions that determine the properties that characterize the CF3 reorientation. The molecule is of no particular interest; it simply provides a motionless backbone (on the nuclear magnetic resonance(NMR) time scale) to investigate CF3 reorientation occurring on the NMR time scale. The effects of (19)F-(19)F and (19)F-(1)H spin-spin dipolar interactions on the complicated nonexponential NMRrelaxation provide independent inputs into determining a model for CF3 reorientation. As such, these experiments provide much more information than when only one spin species (usually (1)H) is present. In Sec. IV, which can be read immediately after the Introduction without reading the rest of the paper, we compare the barrier to CH3 and CF3 reorientation in seven organic solids and separate this barrier into intramolecular and intermolecular components.
Perturbations and quantum relaxation
Kandhadai, Adithya
2016-01-01
We investigate whether small perturbations can cause relaxation to quantum equilibrium over very long timescales. We consider in particular a two-dimensional harmonic oscillator, which can serve as a model of a field mode on expanding space. We assume an initial wave function with small perturbations to the ground state. We present evidence that the trajectories are highly confined so as to preclude relaxation to equilibrium even over very long timescales. Cosmological implications are briefly discussed.
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...
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...
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...... in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization....
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...
Siwko, Magdalena E; Corni, Stefano
2013-04-28
Proteins immobilized on inorganic surfaces are important in technological fields such as biosensors, enzymatic biofuel cells and biomolecular electronics. In these frameworks, it has been demonstrated that some proteins are able to keep their functionality, although the latter may be somewhat modified by the interaction with the surface. Cytochrome C, an heme-based electron transfer protein, has been found to be able to exchange electrons with the gold surface on which it is immobilized, but some deviations from the expected electron transfer rates were evidenced [C. A. Bortolotti, et al., J. Phys. Chem. C 2007, 111, 12100-12105]. In this work we have used molecular dynamics simulations of (native and mutated) yeast cytochrome C supported on Au(111) to investigate the microscopic picture behind the experimental behavior of the molecule. In particular, we have focused on the structural re-arrangements due to the interactions with the surface. We found that, despite being secondary-structure preserving, they can profoundly affect protein-surface electronic coupling and, in turn, electron transfer rates, explaining experimental findings. The conformational flexibility of the protein in the region of the protein-surface bond is thus pivotal in determining the resulting ET functionality of the immobilized protein.
Nuclear spin relaxation in liquids theory, experiments, and applications
Kowalewski, Jozef
2006-01-01
Nuclear magnetic resonance (NMR) is widely used across many fields because of the rich data it produces, and some of the most valuable data come from the study of nuclear spin relaxation in solution. While described to varying degrees in all major NMR books, spin relaxation is often perceived as a difficult, if not obscure, topic, and an accessible, cohesive treatment has been nearly impossible to find.Collecting relaxation theory, experimental techniques, and illustrative applications into a single volume, this book clarifies the nature of the phenomenon, shows how to study it, and explains why such studies are worthwhile. Coverage ranges from basic to rigorous theory and from simple to sophisticated experimental methods, and the level of detail is somewhat greater than most other NMR texts. Topics include cross-relaxation, multispin phenomena, relaxation studies of molecular dynamics and structure, and special topics such as relaxation in systems with quadrupolar nuclei and paramagnetic systems.Avoiding ove...
Excited-state relaxation of some aminoquinolines
Directory of Open Access Journals (Sweden)
2006-01-01
Full Text Available The absorption and fluorescence spectra, fluorescence quantum yields and lifetimes, and fluorescence rate constants ( k f of 2-amino-3-( 2 ′ -benzoxazolylquinoline (I, 2-amino-3-( 2 ′ -benzothiazolylquinoline (II, 2-amino-3-( 2 ′ -methoxybenzothiazolyl-quinoline (III, 2-amino-3-( 2 ′ -benzothiazolylbenzoquinoline (IV at different temperatures have been measured. The shortwavelength shift of fluorescence spectra of compounds studied (23–49 nm in ethanol as the temperature decreases (the solvent viscosity increases points out that the excited-state relaxation process takes place. The rate of this process depends essentially on the solvent viscosity, but not the solvent polarity. The essential increasing of fluorescence rate constant k f (up to about 7 times as the solvent viscosity increases proves the existence of excited-state structural relaxation consisting in the mutual internal rotation of molecular fragments of aminoquinolines studied, followed by the solvent orientational relaxation.
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...... 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...
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
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.
Classification using Bayesian neural nets
J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)
1995-01-01
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to neura
Bayesian Intersubjectivity and Quantum Theory
Pérez-Suárez, Marcos; Santos, David J.
2005-02-01
Two of the major approaches to probability, namely, frequentism and (subjectivistic) Bayesian theory, are discussed, together with the replacement of frequentist objectivity for Bayesian intersubjectivity. This discussion is then expanded to Quantum Theory, as quantum states and operations can be seen as structural elements of a subjective nature.
Bayesian Approach for Inconsistent Information.
Stein, M; Beer, M; Kreinovich, V
2013-10-01
In engineering situations, we usually have a large amount of prior knowledge that needs to be taken into account when processing data. Traditionally, the Bayesian approach is used to process data in the presence of prior knowledge. Sometimes, when we apply the traditional Bayesian techniques to engineering data, we get inconsistencies between the data and prior knowledge. These inconsistencies are usually caused by the fact that in the traditional approach, we assume that we know the exact sample values, that the prior distribution is exactly known, etc. In reality, the data is imprecise due to measurement errors, the prior knowledge is only approximately known, etc. So, a natural way to deal with the seemingly inconsistent information is to take this imprecision into account in the Bayesian approach - e.g., by using fuzzy techniques. In this paper, we describe several possible scenarios for fuzzifying the Bayesian approach. Particular attention is paid to the interaction between the estimated imprecise parameters. In this paper, to implement the corresponding fuzzy versions of the Bayesian formulas, we use straightforward computations of the related expression - which makes our computations reasonably time-consuming. Computations in the traditional (non-fuzzy) Bayesian approach are much faster - because they use algorithmically efficient reformulations of the Bayesian formulas. We expect that similar reformulations of the fuzzy Bayesian formulas will also drastically decrease the computation time and thus, enhance the practical use of the proposed methods.
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Bayesian 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.
Bayesian approach to inverse statistical mechanics.
Habeck, Michael
2014-05-01
Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.
Approximate Bayesian computation.
Directory of Open Access Journals (Sweden)
Mikael Sunnåker
Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.
Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science primar
Implementing Bayesian Vector Autoregressions Implementing Bayesian Vector Autoregressions
Directory of Open Access Journals (Sweden)
Richard M. Todd
1988-03-01
Full Text Available Implementing Bayesian Vector Autoregressions This paper discusses how the Bayesian approach can be used to construct a type of multivariate forecasting model known as a Bayesian vector autoregression (BVAR. In doing so, we mainly explain Doan, Littermann, and Sims (1984 propositions on how to estimate a BVAR based on a certain family of prior probability distributions. indexed by a fairly small set of hyperparameters. There is also a discussion on how to specify a BVAR and set up a BVAR database. A 4-variable model is used to iliustrate the BVAR approach.
Bayesian inference reveals ancient origin of simian foamy virus in orangutans.
Reid, Michael J C; Switzer, William M; Schillaci, Michael A; Klegarth, Amy R; Campbell, Ellsworth; Ragonnet, Manon; Joanisse, Isabelle; Caminiti, Kyna; Lowenberger, Carl A; Galdikas, Birute Mary F; Hollocher, Hope; Sandstrom, Paul A; Brooks, James I
2017-03-05
Simian foamy viruses (SFVs) infect most nonhuman primate species and appears to co-evolve with its hosts. This co-evolutionary signal is particularly strong among great apes, including orangutans (genus Pongo). Previous studies have identified three distinct orangutan SFV clades. The first of these three clades is composed of SFV from P. abelii from Sumatra, the second consists of SFV from P. pygmaeus from Borneo, while the third clade is mixed, comprising an SFV strain found in both species of orangutan. The existence of the mixed clade has been attributed to an expansion of P. pygmaeus into Sumatra following the Mount Toba super-volcanic eruption about 73,000years ago. Divergence dating, however, has yet to be performed to establish a temporal association with the Toba eruption. Here, we use a Bayesian framework and a relaxed molecular clock model with fossil calibrations to test the Toba hypothesis and to gain a more complete understanding of the evolutionary history of orangutan SFV. As with previous studies, our results show a similar three-clade orangutan SFV phylogeny, along with strong statistical support for SFV-host co-evolution in orangutans. Using Bayesian inference, we date the origin of orangutan SFV to >4.7 million years ago (mya), while the mixed species clade dates to approximately 1.7mya, >1.6 million years older than the Toba super-eruption. These results, combined with fossil and paleogeographic evidence, suggest that the origin of SFV in Sumatran and Bornean orangutans, including the mixed species clade, likely occurred on the mainland of Indo-China during the Late Pliocene and Calabrian stage of the Pleistocene, respectively.
Kinetic Actviation Relaxation Technique
Béland, Laurent Karim; El-Mellouhi, Fedwa; Joly, Jean-François; Mousseau, Normand
2011-01-01
We present a detailed description of the kinetic Activation-Relaxation Technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si, self-interstitial diffusion in Fe and structural relaxation in amorphous silicon.
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)
Nonlinear fractional relaxation
Indian Academy of Sciences (India)
A Tofighi
2012-04-01
We deﬁne a nonlinear model for fractional relaxation phenomena. We use -expansion method to analyse this model. By studying the fundamental solutions of this model we ﬁnd that when → 0 the model exhibits a fast decay rate and when → ∞ the model exhibits a power-law decay. By analysing the frequency response we ﬁnd a logarithmic enhancement for the relative ratio of susceptibility.
Ortega, Pedro A
2011-01-01
Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans use strong prior knowledge; and rather than encoding hard causal relationships, they encode beliefs over causal structures, allowing for sound generalization from the observations they obtain from directly acting in the world. In this work we propose a Bayesian approach to causal induction which allows modeling beliefs over multiple causal hypotheses and predicting the behavior of the world under causal interventions. We then illustrate how this method extracts causal information from data containing interventions and observations.
Blundell, Charles; Heller, Katherine A
2012-01-01
Hierarchical structure is ubiquitous in data across many domains. There are many hier- archical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these meth- ods limit discoverable hierarchies to those with binary branching structure. This lim- itation, while computationally convenient, is often undesirable. In this paper we ex- plore a Bayesian hierarchical clustering algo- rithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy ag- glomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.
Bayesian inference in geomagnetism
Backus, George E.
1988-01-01
The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.
Dynamic isotope effects on relaxation of quadrupolar nuclei in 12 simple organic molecules
Institute of Scientific and Technical Information of China (English)
毛希安; andM.Holz
1995-01-01
Dynamic isotope effects on relaxation rate of quadrupolar nuclei are preliminarily reported. The relaxation rates of 17O and 14N in 12 simple organic molecules and their 18 corresponding deuterated species have been systematically measured. The principal components of the molecular inertia tensors have been calculated. The results show that there is an intrinsic correlation between the dynamic isotope effects of the relaxation rate and the static isotope effects of the molecular inertia. The concepts of molecular collision frequency and translation-rotation coupling have been introduced into the NMR relaxation theory. Therefore, a reasonable explanation of the experimental results has been given.
Microscopic Origin of Shear Relaxation in a Model Viscoelastic Liquid
Ashwin, J.; Sen, Abhijit
2015-02-01
An atomistic description of shear stress relaxation in a viscoelastic liquid is developed from first principles through accurate molecular dynamic simulations in a model Yukawa system. It is shown that the relaxation time τMex of the excess part of the shear stress autocorrelation function provides a correct measure of the relaxation process. Below a certain critical value Γc of the Coulomb coupling strength, the lifetime of local atomic connectivity τLC converges to τMex and is the microscopic origin of the relaxation. At Γ ≫Γc, i.e., in the potential energy dominated regime, τMex→τM (the Maxwell relaxation time) and can, therefore, fully account for the elastic or "solidlike" behavior. Our results can help provide a better fundamental understanding of viscoelastic behavior in a variety of strongly coupled systems such as dusty plasmas, colloids, and non-Newtonian fluids.
Microscopic origin of shear relaxation in a model viscoelastic liquid.
Ashwin, J; Sen, Abhijit
2015-02-01
An atomistic description of shear stress relaxation in a viscoelastic liquid is developed from first principles through accurate molecular dynamic simulations in a model Yukawa system. It is shown that the relaxation time τ(M)(ex) of the excess part of the shear stress autocorrelation function provides a correct measure of the relaxation process. Below a certain critical value Γ(c) of the Coulomb coupling strength, the lifetime of local atomic connectivity τ(LC) converges to τ(M)(ex) and is the microscopic origin of the relaxation. At Γ≫Γ(c), i.e., in the potential energy dominated regime, τ(M)(ex)→τ(M) (the Maxwell relaxation time) and can, therefore, fully account for the elastic or "solidlike" behavior. Our results can help provide a better fundamental understanding of viscoelastic behavior in a variety of strongly coupled systems such as dusty plasmas, colloids, and non-Newtonian fluids.
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
Irregular-Time Bayesian Networks
Ramati, Michael
2012-01-01
In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of t...
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
Bayesian Methods for Statistical Analysis
Puza, Borek
2015-01-01
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete c...
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...
Ultrafast vibrational energy relaxation of the water bridge.
Piatkowski, Lukasz; Wexler, Adam D; Fuchs, Elmar C; Schoenmaker, Hinco; Bakker, Huib J
2012-05-14
We report the energy relaxation of the OH stretch vibration of HDO molecules contained in an HDO:D(2)O water bridge using femtosecond mid-infrared pump-probe spectroscopy. We found that the vibrational lifetime is shorter (~630 ± 50 fs) than for HDO molecules in bulk HDO:D(2)O (~740 ± 40 fs). In contrast, the thermalization dynamics following the vibrational relaxation are much slower (~1.5 ± 0.4 ps) than in bulk HDO:D(2)O (~250 ± 90 fs). These differences in energy relaxation dynamics strongly indicate that the water bridge and bulk water differ on a molecular scale.
Relaxation dynamics of amorphous dibucaine using dielectric studies
Sahra, M.; Jumailath, K.; Thayyil, M. Shahin; Capaccioli, S.
2015-06-01
Using broadband dielectric spectroscopy the molecular mobility of dibucaine is investigated in the supercooled liquid and gassy states, over a wide temperature range for some test frequencies. Above the glass transition temperature Tg, the presence of structural α- relaxation peak was observed due to the cooperative motions of the molecule and upon cooling frozen kinetically to form the glass. The secondary relaxation process was perceivable below Tg due to localized motions. The peak loss frequency of α-relaxation process shows non-Arrhenius behavior and obeys Vogel-Fulcher-Tammann equation over the measured temperature range whereas the β- process shows Arrhenius behavior.
Werbeck, Nicolas D; Hansen, D. Flemming
2014-01-01
The equations that describe the time-evolution of transverse and longitudinal 15N magnetisations in tetrahedral ammonium ions, 15NH4 +, are derived from the Bloch-Wangsness-Redfield density operator relaxation theory. It is assumed that the relaxation of the spin-states is dominated by (1) the intra-molecular 15N–1H and 1H–1H dipole–dipole interactions and (2) interactions of the ammonium protons with remote spins, which also include the contribution to the relaxations that arise from the exc...
Grueneisen relaxation photoacoustic microscopy
Wang, Lidai; Zhang, Chi; Wang, Lihong V.
2014-01-01
The temperature-dependent property of the Grueneisen parameter has been employed in photoacoustic imaging mainly to measure tissue temperature. Here we explore this property using a different approach and develop Grueneisen-relaxation photoacoustic microscopy (GR-PAM), a technique that images non-radiative absorption with confocal optical resolution. GR-PAM sequentially delivers two identical laser pulses with a micro-second-scale time delay. The first laser pulse generates a photoacoustic signal and thermally tags the in-focus absorbers. Owing to the temperature dependence of the Grueneisen parameter, when the second laser pulse excites the tagged absorbers within the thermal relaxation time, a photoacoustic signal stronger than the first one is produced. GR-PAM detects the amplitude difference between the two co-located photoacoustic signals, confocally imaging the non-radiative absorption. We greatly improved axial resolution from 45 µm to 2.3 µm and at the same time slightly improved lateral resolution from 0.63 µm to 0.41 µm. In addition, the optical sectioning capability facilitates the measurement of the absolute absorption coefficient without fluence calibration. PMID:25379919
Dynamic Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2011-01-01
Bayesian optimization (BO) algorithms try to optimize an unknown function that is expensive to evaluate using minimum number of evaluations/experiments. Most of the proposed algorithms in BO are sequential, where only one experiment is selected at each iteration. This method can be time inefficient when each experiment takes a long time and more than one experiment can be ran concurrently. On the other hand, requesting a fix-sized batch of experiments at each iteration causes performance inefficiency in BO compared to the sequential policies. In this paper, we present an algorithm that asks a batch of experiments at each time step t where the batch size p_t is dynamically determined in each step. Our algorithm is based on the observation that the sequence of experiments selected by the sequential policy can sometimes be almost independent from each other. Our algorithm identifies such scenarios and request those experiments at the same time without degrading the performance. We evaluate our proposed method us...
A Bayesian variable selection procedure to rank overlapping gene sets
Directory of Open Access Journals (Sweden)
Skarman Axel
2012-05-01
Full Text Available Abstract Background Genome-wide expression profiling using microarrays or sequence-based technologies allows us to identify genes and genetic pathways whose expression patterns influence complex traits. Different methods to prioritize gene sets, such as the genes in a given molecular pathway, have been described. In many cases, these methods test one gene set at a time, and therefore do not consider overlaps among the pathways. Here, we present a Bayesian variable selection method to prioritize gene sets that overcomes this limitation by considering all gene sets simultaneously. We applied Bayesian variable selection to differential expression to prioritize the molecular and genetic pathways involved in the responses to Escherichia coli infection in Danish Holstein cows. Results We used a Bayesian variable selection method to prioritize Kyoto Encyclopedia of Genes and Genomes pathways. We used our data to study how the variable selection method was affected by overlaps among the pathways. In addition, we compared our approach to another that ignores the overlaps, and studied the differences in the prioritization. The variable selection method was robust to a change in prior probability and stable given a limited number of observations. Conclusions Bayesian variable selection is a useful way to prioritize gene sets while considering their overlaps. Ignoring the overlaps gives different and possibly misleading results. Additional procedures may be needed in cases of highly overlapping pathways that are hard to prioritize.
Walczak, Michał; Grubmüller, Helmut
2014-08-01
We developed a Bayesian method to extract macromolecular structure information from sparse single-molecule x-ray free-electron laser diffraction images. The method addresses two possible scenarios. First, using a "seed" structural model, the molecular orientation is determined for each of the provided diffraction images, which are then averaged in three-dimensional reciprocal space. Subsequently, the real space electron density is determined using a relaxed averaged alternating reflections algorithm. In the second approach, the probability that the "seed" model fits to the given set of diffraction images as a whole is determined and used to distinguish between proposed structures. We show that for a given x-ray intensity, unexpectedly, the achievable resolution increases with molecular mass such that structure determination should be more challenging for small molecules than for larger ones. For a sufficiently large number of recorded photons (>200) per diffraction image an M^{1/6} scaling is seen. Using synthetic diffraction data for a small glutathione molecule as a challenging test case, successful determination of electron density was demonstrated for 20000 diffraction patterns with random orientations and an average of 82 elastically scattered and recorded photons per image, also in the presence of up to 50% background noise. The second scenario is exemplified and assessed for three biomolecules of different sizes. In all cases, determining the probability of a structure given set of diffraction patterns allowed successful discrimination between different conformations of the test molecules. A structure model of the glutathione tripeptide was refined in a Monte Carlo simulation from a random starting conformation. Further, effective distinguishing between three differently arranged immunoglobulin domains of a titin molecule and also different states of a ribosome in a tRNA translocation process was demonstrated. These results show that the proposed method is
Magnetoviscosity and relaxation in ferrofluids
Felderhof
2000-09-01
The increase in viscosity of a ferrofluid due to an applied magnetic field is discussed on the basis of a phenomenological relaxation equation for the magnetization. The relaxation equation was derived earlier from irreversible thermodynamics, and differs from that postulated by Shliomis. The two relaxation equations lead to a different dependence of viscosity on magnetic field, unless the relaxation rates are related in a specific field-dependent way. Both planar Couette flow and Poiseuille pipe flow in parallel and perpendicular magnetic field are discussed. The entropy production for these situations is calculated and related to the magnetoviscosity.
Rupp, Wolf; Simon, Karl-Heinz; Bohnert, Michael
2009-01-01
Complete relaxation can be achieved by floating in a darkened, sound-proof relaxation tank filled with salinated water kept at body temperature. Under these conditions, meditation exercises up to self-hypnosis may lead to deep relaxation with physical and mental revitalization. A user manipulated his tank, presumably to completely cut off all optical and acoustic stimuli and accidentally also covered the ventilation hole. The man was found dead in his relaxation tank. The findings suggested lack of oxygen as the cause of death.
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
Bayesian microsaccade detection
Mihali, Andra; van Opheusden, Bas; Ma, Wei Ji
2017-01-01
Microsaccades are high-velocity fixational eye movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity exceeds a threshold. We have developed a new method, Bayesian microsaccade detection (BMD), which performs inference based on a simple statistical model of eye positions. In this model, a hidden state variable changes between drift and microsaccade states at random times. The eye position is a biased random walk with different velocity distributions for each state. BMD generates samples from the posterior probability distribution over the eye state time series given the eye position time series. Applied to simulated data, BMD recovers the “true” microsaccades with fewer errors than alternative algorithms, especially at high noise. Applied to EyeLink eye tracker data, BMD detects almost all the microsaccades detected by the default method, but also apparent microsaccades embedded in high noise—although these can also be interpreted as false positives. Next we apply the algorithms to data collected with a Dual Purkinje Image eye tracker, whose higher precision justifies defining the inferred microsaccades as ground truth. When we add artificial measurement noise, the inferences of all algorithms degrade; however, at noise levels comparable to EyeLink data, BMD recovers the “true” microsaccades with 54% fewer errors than the default algorithm. Though unsuitable for online detection, BMD has other advantages: It returns probabilities rather than binary judgments, and it can be straightforwardly adapted as the generative model is refined. We make our algorithm available as a software package. PMID:28114483
Tang, An-Min; Tang, Nian-Sheng
2015-02-28
We propose a semiparametric multivariate skew-normal joint model for multivariate longitudinal and multivariate survival data. One main feature of the posited model is that we relax the commonly used normality assumption for random effects and within-subject error by using a centered Dirichlet process prior to specify the random effects distribution and using a multivariate skew-normal distribution to specify the within-subject error distribution and model trajectory functions of longitudinal responses semiparametrically. A Bayesian approach is proposed to simultaneously obtain Bayesian estimates of unknown parameters, random effects and nonparametric functions by combining the Gibbs sampler and the Metropolis-Hastings algorithm. Particularly, a Bayesian local influence approach is developed to assess the effect of minor perturbations to within-subject measurement error and random effects. Several simulation studies and an example are presented to illustrate the proposed methodologies.
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
Bayesian 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...
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
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 perception, and use this observation to frame a new computational account of the need for, and action of, attention - unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments......, and 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...
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 Missile System Reliability from Point Estimates
2014-10-28
OCT 2014 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Bayesian Missile System Reliability from Point Estimates 5a. CONTRACT...Principle (MEP) to convert point estimates to probability distributions to be used as priors for Bayesian reliability analysis of missile data, and...illustrate this approach by applying the priors to a Bayesian reliability model of a missile system. 15. SUBJECT TERMS priors, Bayesian , missile
Magnetic relaxation in anisotropic magnets
DEFF Research Database (Denmark)
Lindgård, Per-Anker
1971-01-01
The line shape and the kinematic and thermodynamic slowing down of the critical and paramagnetic relaxation in axially anisotropic materials are discussed. Kinematic slowing down occurs only in the longitudinal relaxation function. The thermodynamic slowing down occurs in either the transverse or...
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
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....
Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.
Echinaka, Yuki; Ozeki, Yukiyasu
2016-10-01
The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
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
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 b...
Structural relaxation in viscous metallic liquids
Energy Technology Data Exchange (ETDEWEB)
Meyer, A. [National Inst. of Standards and Technology (BFRL), Gaithersburg, MD (United States)]|[Technische Univ. Muenchen, Muenchen (Germany); Wuttke, J.; Petry, W. [Technische Univ. Muenchen, Muenchen (Germany); Schober, H. [Institut Max von Laue - Paul Langevin (ILL), 38 - Grenoble (France); Randl, O.G. [Manufacture Michelin, Clermont-Ferrand (France)
1999-11-01
Recently, metallic alloys have been found that exhibit extremely large viscosities in the liquid state. These liquids can be quenched into bulk metallic glasses at relatively modest cooling rates. In contrast to simple metals the structural relaxation of these systems show a two step decay in the liquid state. This behaviour has long been known for molecular or ionic glass formers in their under-cooled liquid state. Applying an analysis previously used for the glass formers (mode-coupling theory) a full quantitative description of the neutron data is obtained for these metallic liquids. (authors) 3 refs., 2 figs.
Bayesian stable isotope mixing models
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 mixtur...
Naive Bayesian for Email Filtering
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The paper presents a method of email filter based on Naive Bayesian theory that can effectively filter junk mail and illegal mail. Furthermore, the keys of implementation are discussed in detail. The filtering model is obtained from training set of email. The filtering can be done without the users specification of filtering rules.
Bayesian analysis of binary sequences
Torney, David C.
2005-03-01
This manuscript details Bayesian methodology for "learning by example", with binary n-sequences encoding the objects under consideration. Priors prove influential; conformable priors are described. Laplace approximation of Bayes integrals yields posterior likelihoods for all n-sequences. This involves the optimization of a definite function over a convex domain--efficiently effectuated by the sequential application of the quadratic program.
Bayesian NL interpretation and learning
Zeevat, H.
2011-01-01
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language
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 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...
Bayesian Classification of Image Structures
DEFF Research Database (Denmark)
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
2009-01-01
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...
3-D contextual Bayesian classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further...
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...
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...
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 Evidence and Model Selection
Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben
2014-01-01
In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
Bayesian Alternation During Tactile Augmentation
Directory of Open Access Journals (Sweden)
Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
Bayesian analysis of rare events
Energy Technology Data Exchange (ETDEWEB)
Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
Can Black Hole Relax Unitarily?
Solodukhin, S. N.
2005-03-01
We review the way the BTZ black hole relaxes back to thermal equilibrium after a small perturbation and how it is seen in the boundary (finite volume) CFT. The unitarity requires the relaxation to be quasi-periodic. It is preserved in the CFT but is not obvious in the case of the semiclassical black hole the relaxation of which is driven by complex quasi-normal modes. We discuss two ways of modifying the semiclassical black hole geometry to maintain unitarity: the (fractal) brick wall and the worm-hole modification. In the latter case the entropy comes out correctly as well.
Can Black Hole Relax Unitarily?
Solodukhin, S N
2004-01-01
We review the way the BTZ black hole relaxes back to thermal equilibrium after a small perturbation and how it is seen in the boundary (finite volume) CFT. The unitarity requires the relaxation to be quasi-periodic. It is preserved in the CFT but is not obvious in the case of the semiclassical black hole the relaxation of which is driven by complex quasi-normal modes. We discuss two ways of modifying the semiclassical black hole geometry to maintain unitarity: the (fractal) brick wall and the worm-hole modification. In the latter case the entropy comes out correctly as well.
STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA.
Directory of Open Access Journals (Sweden)
AHLAM LABDAOUI
2012-12-01
Full Text Available The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computational tools used in modern Bayesian econometrics. Some of the most important methods of posterior simulation are Monte Carlo integration, importance sampling, Gibbs sampling and the Metropolis- Hastings algorithm. The Bayesian should also be able to put the theory and computational tools together in the context of substantive empirical problems. We focus primarily on recent developments in Bayesian computation. Then we focus on particular models. Inevitably, we combine theory and computation in the context of particular models. Although we have tried to be reasonably complete in terms of covering the basic ideas of Bayesian theory and the computational tools most commonly used by the Bayesian, there is no way we can cover all the classes of models used in econometrics. We propose to the user of analysis of variance and linear regression model.
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...
Protein dynamics: from rattling in a cage to structural relaxation
Khodadadia, S.; Sokolov, A.P.
2015-01-01
We present an overview of protein dynamics based mostly on results of neutron scattering, dielectric relaxation spectroscopy and molecular dynamics simulations. We identify several major classes of protein motions on the time scale from faster than picoseconds to several microseconds, and discuss th
An Exact Relaxation of Clustering
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai
2009-01-01
of clustering problems such as the K-means objective and pairwise clustering as well as graph partition problems, e.g., for community detection in complex networks. In particular we show that a relaxation to the simplex can be given for which the extreme solutions are stable hard assignment solutions and vice......Continuous relaxation of hard assignment clustering problems can lead to better solutions than greedy iterative refinement algorithms. However, the validity of existing relaxations is contingent on problem specific fuzzy parameters that quantify the level of similarity between the original...... versa. Based on the new relaxation we derive the SR-clustering algorithm that has the same complexity as traditional greedy iterative refinement algorithms but leading to significantly better partitions of the data. A Matlab implementation of the SR-clustering algorithm is available for download....
Elasticity, structure, and relaxation of extended proteins under force
2013-01-01
Force spectroscopies have emerged as a powerful and unprecedented tool to study and manipulate biomolecules directly at a molecular level. Usually, protein and DNA behavior under force is described within the framework of the worm-like chain (WLC) model for polymer elasticity. Although it has been surprisingly successful for the interpretation of experimental data, especially at high forces, the WLC model lacks structural and dynamical molecular details associated with protein relaxation unde...
Negative magnetic relaxation in superconductors
Directory of Open Access Journals (Sweden)
Krasnoperov E.P.
2013-01-01
Full Text Available It was observed that the trapped magnetic moment of HTS tablets or annuli increases in time (negative relaxation if they are not completely magnetized by a pulsed magnetic field. It is shown, in the framework of the Bean critical-state model, that the radial temperature gradient appearing in tablets or annuli during a pulsed field magnetization can explain the negative magnetic relaxation in the superconductor.
Relaxation of a Simulated Lipid Bilayer Vesicle Compressed by an AFM
Barlow, Ben M; Joos, Béla
2016-01-01
Using Coarse-Grained Molecular Dynamics simulations, we study the relaxation of bilayer vesicles, uniaxially compressed by an Atomic Force Microscope (AFM) cantilever. The relaxation time exhibits a strong force-dependence. Force-compression curves are very similar to recent experiments wherein giant unilamellar vesicles were compressed in a nearly identical manner.
Elsken, J. van der; Frenkel, D.
1977-01-01
Many molecular relaxation processes in fluids are sensitive to the time-dependence of local, anisotropic density fluctuations. The role played by anisotropic density fluctuations in the rotational relaxation of a linear, quantized rotor will be discussed in some detail. An expression for the dipolec
Relaxation of a simulated lipid bilayer vesicle compressed by an atomic force microscope
Barlow, Ben M.; Bertrand, Martine; Joós, Béla
2016-11-01
Using coarse-grained molecular dynamics simulations, we study the relaxation of bilayer vesicles, uniaxially compressed by an atomic force microscope cantilever. The relaxation time exhibits a strong force dependence. Force-compression curves are very similar to recent experiments wherein giant unilamellar vesicles were compressed in a nearly identical manner.
Relaxation Time of the Particle Beam with an Anisotropic Velocity Distribution
Directory of Open Access Journals (Sweden)
V.P. Vechirka
2012-11-01
Full Text Available The computer experiment for study of the relaxation time of the beam particles with an anisotropic velocity distribution is performed by the molecular dynamics. Obtained results agree with the characteristic times of thermal relaxation in plasma for the electronic coolers in modern storage rings.
Shape Dependence of Low-Temperature Magnetic Relaxation of Mn12Ac
Institute of Scientific and Technical Information of China (English)
LIU Hai-Qing; SU Shao-Kui; JING Xiu-nian; LIU Ying; LI Yan-rong; HE Lun-Hua; GE Pei-Wen; YAN Qi-Wei; WANG Yun-Ping
2008-01-01
We report the discovery that the low-temperature magnetic relaxation in Mn,12 Ac single crystals strongly depends on the shape of the samples. The relaxation time exhibits a minimum at the phase transition point between ferromagnetic and antiferromagnetic phases. The shape dependence is attributed to the dipolar interaction between molecular magnets.
Stress relaxation following uniaxial extension of polystyrene melt and oligomer dilutions
DEFF Research Database (Denmark)
Huang, Qian; Rasmussen, Henrik K.
2016-01-01
The filament stretching rheometer has been used to measure the stress relaxation following the startup of uniaxial extensional flow, on anarrow molar mass distribution (NMMD) polystyrene melt and styrene oligomer dilutions thereof. All samples used here were characterizedin molecular weight, mech...... ofconstitutive representation was observed for all measured relaxations.VC 2016 The Society of Rheology....
Difference and similarity of dielectric relaxation processes among polyols
Minoguchi, Ayumi; Kitai, Kei; Nozaki, Ryusuke
2003-09-01
Complex permittivity measurements were performed on sorbitol, xylitol, and sorbitol-xylitol mixture in the supercooled liquid state in an extremely wide frequency range from 10 μHz to 500 MHz at temperatures near and above the glass transition temperature. We determined detailed behavior of the relaxation parameters such as relaxation frequency and broadening against temperature not only for the α process but also for the β process above the glass transition temperature, to the best of our knowledge, for the first time. Since supercooled liquids are in the quasi-equilibrium state, the behavior of all the relaxation parameters for the β process can be compared among the polyols as well as those for the α process. The relaxation frequencies of the α processes follow the Vogel-Fulcher-Tammann manner and the loci in the Arrhenius diagram are different corresponding to the difference of the glass transition temperatures. On the other hand, the relaxation frequencies of the β processes, which are often called as the Johari-Goldstein processes, follow the Arrhenius-type temperature dependence. The relaxation parameters for the β process are quite similar among the polyols at temperatures below the αβ merging temperature, TM. However, they show anomalous behavior near TM, which depends on the molecular size of materials. These results suggest that the origin of the β process is essentially the same among the polyols.
Bayesian versus 'plain-vanilla Bayesian' multitarget statistics
Mahler, Ronald P. S.
2004-08-01
Finite-set statistics (FISST) is a direct generalization of single-sensor, single-target Bayes statistics to the multisensor-multitarget realm, based on random set theory. Various aspects of FISST are being investigated by several research teams around the world. In recent years, however, a few partisans have claimed that a "plain-vanilla Bayesian approach" suffices as down-to-earth, "straightforward," and general "first principles" for multitarget problems. Therefore, FISST is mere mathematical "obfuscation." In this and a companion paper I demonstrate the speciousness of these claims. In this paper I summarize general Bayes statistics, what is required to use it in multisensor-multitarget problems, and why FISST is necessary to make it practical. Then I demonstrate that the "plain-vanilla Bayesian approach" is so heedlessly formulated that it is erroneous, not even Bayesian denigrates FISST concepts while unwittingly assuming them, and has resulted in a succession of algorithms afflicted by inherent -- but less than candidly acknowledged -- computational "logjams."
Relaxation Dynamics of Nanoparticle-Tethered Polymer Chains
Kim, Sung A
2015-09-08
© 2015 American Chemical Society. Relaxation dynamics of nanoparticle-tethered cis-1,4-polyisoprene (PI) are investigated using dielectric spectroscopy and rheometry. A model system composed of polymer chains densely grafted to spherical SiO
Bayesian phylogenetic estimation of fossil ages
Drummond, Alexei J.; Stadler, Tanja
2016-01-01
Recent advances have allowed for both morphological fossil evidence and molecular sequences to be integrated into a single combined inference of divergence dates under the rule of Bayesian probability. In particular, the fossilized birth–death tree prior and the Lewis-Mk model of discrete morphological evolution allow for the estimation of both divergence times and phylogenetic relationships between fossil and extant taxa. We exploit this statistical framework to investigate the internal consistency of these models by producing phylogenetic estimates of the age of each fossil in turn, within two rich and well-characterized datasets of fossil and extant species (penguins and canids). We find that the estimation accuracy of fossil ages is generally high with credible intervals seldom excluding the true age and median relative error in the two datasets of 5.7% and 13.2%, respectively. The median relative standard error (RSD) was 9.2% and 7.2%, respectively, suggesting good precision, although with some outliers. In fact, in the two datasets we analyse, the phylogenetic estimate of fossil age is on average less than 2 Myr from the mid-point age of the geological strata from which it was excavated. The high level of internal consistency found in our analyses suggests that the Bayesian statistical model employed is an adequate fit for both the geological and morphological data, and provides evidence from real data that the framework used can accurately model the evolution of discrete morphological traits coded from fossil and extant taxa. We anticipate that this approach will have diverse applications beyond divergence time dating, including dating fossils that are temporally unconstrained, testing of the ‘morphological clock', and for uncovering potential model misspecification and/or data errors when controversial phylogenetic hypotheses are obtained based on combined divergence dating analyses. This article is part of the themed issue ‘Dating species divergences
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 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.
Deep Learning and Bayesian Methods
Directory of Open Access Journals (Sweden)
Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Deep Learning and Bayesian Methods
Prosper, Harrison B.
2017-03-01
A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian Source Separation and Localization
Knuth, K H
1998-01-01
The problem of mixed signals occurs in many different contexts; one of the most familiar being acoustics. The forward problem in acoustics consists of finding the sound pressure levels at various detectors resulting from sound signals emanating from the active acoustic sources. The inverse problem consists of using the sound recorded by the detectors to separate the signals and recover the original source waveforms. In general, the inverse problem is unsolvable without additional information. This general problem is called source separation, and several techniques have been developed that utilize maximum entropy, minimum mutual information, and maximum likelihood. In previous work, it has been demonstrated that these techniques can be recast in a Bayesian framework. This paper demonstrates the power of the Bayesian approach, which provides a natural means for incorporating prior information into a source model. An algorithm is developed that utilizes information regarding both the statistics of the amplitudes...
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.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available 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.
Bayesian analysis for kaon photoproduction
Energy Technology Data Exchange (ETDEWEB)
Marsainy, T., E-mail: tmart@fisika.ui.ac.id; Mart, T., E-mail: tmart@fisika.ui.ac.id [Department Fisika, FMIPA, Universitas Indonesia, Depok 16424 (Indonesia)
2014-09-25
We have investigated contribution of the nucleon resonances in the kaon photoproduction process by using an established statistical decision making method, i.e. the Bayesian method. This method does not only evaluate the model over its entire parameter space, but also takes the prior information and experimental data into account. The result indicates that certain resonances have larger probabilities to contribute to the process.
Bayesian priors and nuisance parameters
Gupta, Sourendu
2016-01-01
Bayesian techniques are widely used to obtain spectral functions from correlators. We suggest a technique to rid the results of nuisance parameters, ie, parameters which are needed for the regularization but cannot be determined from data. We give examples where the method works, including a pion mass extraction with two flavours of staggered quarks at a lattice spacing of about 0.07 fm. We also give an example where the method does not work.
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
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.
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
. This 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....... It is shown how half-cell potential measurements may be utilized to update the probability of excessive repair after 50 years....
Energy Technology Data Exchange (ETDEWEB)
Wang, Xianlong, E-mail: WangXianlong@uestc.edu.cn, E-mail: pbeckman@brynmawr.edu [Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, 4 North Jianshe Rd., 2nd Section, Chengdu 610054 (China); Mallory, Frank B. [Department of Chemistry, Bryn Mawr College, 101 North Merion Ave., Bryn Mawr, Pennsylvania 19010-2899 (United States); Mallory, Clelia W. [Department of Chemistry, Bryn Mawr College, 101 North Merion Ave., Bryn Mawr, Pennsylvania 19010-2899 (United States); Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6323 (United States); Odhner, Hosanna R.; Beckmann, Peter A., E-mail: WangXianlong@uestc.edu.cn, E-mail: pbeckman@brynmawr.edu [Department of Physics, Bryn Mawr College, 101 North Merion Ave., Bryn Mawr, Pennsylvania 19010-2899 (United States)
2014-05-21
We report ab initio density functional theory electronic structure calculations of rotational barriers for t-butyl groups and their constituent methyl groups both in the isolated molecules and in central molecules in clusters built from the X-ray structure in four t-butyl aromatic compounds. The X-ray structures have been reported previously. We also report and interpret the temperature dependence of the solid state {sup 1}H nuclear magnetic resonance spin-lattice relaxation rate at 8.50, 22.5, and 53.0 MHz in one of the four compounds. Such experiments for the other three have been reported previously. We compare the computed barriers for methyl group and t-butyl group rotation in a central target molecule in the cluster with the activation energies determined from fitting the {sup 1}H NMR spin-lattice relaxation data. We formulate a dynamical model for the superposition of t-butyl group rotation and the rotation of the t-butyl group's constituent methyl groups. The four compounds are 2,7-di-t-butylpyrene, 1,4-di-t-butylbenzene, 2,6-di-t-butylnaphthalene, and 3-t-butylchrysene. We comment on the unusual ground state orientation of the t-butyl groups in the crystal of the pyrene and we comment on the unusually high rotational barrier of these t-butyl groups.
Bayesian Model Selection with Network Based Diffusion Analysis.
Whalen, Andrew; Hoppitt, William J E
2016-01-01
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.
Reduced complexity turbo equalization using a dynamic Bayesian network
Myburgh, Hermanus C.; Olivier, Jan C.; van Zyl, Augustinus J.
2012-12-01
It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.
A model for the generic alpha relaxation in viscous liquids
DEFF Research Database (Denmark)
Dyre, Jeppe
2005-01-01
. Rev. Lett., 86 (2001) 1271]. Assuming that long-wavelength fluctuations dominate the dynamics, a model for the dielectric alpha relaxation based on the simplest coupling between the density and dipole density fields is proposed here. The model, which is solved in second-order perturbation theory......Dielectric measurements on molecular liquids just above the glass transition indicate that alpha relaxation is characterized by a generic high-frequency loss varying as one over square root of frequency, whereas deviations from this come from one or more low-lying beta processes [Olsen et al., Phys...
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...
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 Option Pricing Framework with Stochastic Volatility for FX Data
Directory of Open Access Journals (Sweden)
Ying Wang
2016-12-01
Full Text Available The application of stochastic volatility (SV models in the option pricing literature usually assumes that the market has sufficient option data to calibrate the model’s risk-neutral parameters. When option data are insufficient or unavailable, market practitioners must estimate the model from the historical returns of the underlying asset and then transform the resulting model into its risk-neutral equivalent. However, the likelihood function of an SV model can only be expressed in a high-dimensional integration, which makes the estimation a highly challenging task. The Bayesian approach has been the classical way to estimate SV models under the data-generating (physical probability measure, but the transformation from the estimated physical dynamic into its risk-neutral counterpart has not been addressed. Inspired by the generalized autoregressive conditional heteroskedasticity (GARCH option pricing approach by Duan in 1995, we propose an SV model that enables us to simultaneously and conveniently perform Bayesian inference and transformation into risk-neutral dynamics. Our model relaxes the normality assumption on innovations of both return and volatility processes, and our empirical study shows that the estimated option prices generate realistic implied volatility smile shapes. In addition, the volatility premium is almost flat across strike prices, so adding a few option data to the historical time series of the underlying asset can greatly improve the estimation of option prices.
Bayesian Inversion of Seabed Scattering Data
2014-09-30
Bayesian Inversion of Seabed Scattering Data (Special Research Award in Ocean Acoustics) Gavin A.M.W. Steininger School of Earth & Ocean...project are to carry out joint Bayesian inversion of scattering and reflection data to estimate the in-situ seabed scattering and geoacoustic parameters...valid OMB control number. 1. REPORT DATE 30 SEP 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Bayesian
Anomaly Detection and Attribution Using Bayesian Networks
2014-06-01
UNCLASSIFIED Anomaly Detection and Attribution Using Bayesian Networks Andrew Kirk, Jonathan Legg and Edwin El-Mahassni National Security and...detection in Bayesian networks , en- abling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a... Bayesian network , our algorithm is able to efficiently search for local maxima of data conflict between closely related vari- ables. Benchmark tests using
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 differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Orientational relaxations in solid (1,1,2,2)tetrachloroethane
Tripathi, P.; Mitsari, E.; Romanini, M.; Serra, P.; Tamarit, J. Ll.; Zuriaga, M.; Macovez, R.
2016-04-01
We employ dielectric spectroscopy and molecular dynamic simulations to investigate the dipolar dynamics in the orientationally disordered solid phase of (1,1,2,2)tetrachloroethane. Three distinct orientational dynamics are observed as separate dielectric loss features, all characterized by a simply activated temperature dependence. The slower process, associated to a glassy transition at 156 ± 1 K, corresponds to a cooperative motion by which each molecule rotates by 180° around the molecular symmetry axis through an intermediate state in which the symmetry axis is oriented roughly orthogonally to the initial and final states. Of the other two dipolar relaxations, the intermediate one is the Johari-Goldstein precursor relaxation of the cooperative dynamics, while the fastest process corresponds to an orientational fluctuation of single molecules into a higher-energy orientation. The Kirkwood correlation factor of the cooperative relaxation is of the order of one tenth, indicating that the molecular dipoles maintain on average a strong antiparallel alignment during their collective motion. These findings show that the combination of dielectric spectroscopy and molecular simulations allows studying in great detail the orientational dynamics in molecular solids.
Dielectric relaxation of long-chain glass-forming monohydroxy alcohols
Gao, Yanqin; Tu, Wenkang; Chen, Zeming; Tian, Yongjun; Liu, Riping; Wang, Li-Min
2013-10-01
The dielectric relaxation of two long-chain glass forming monohydroxy alcohols, 2-butyl-1-octanol and 2-hexyl-1-decanol, is studied at low temperature. Remarkable broadening from the pure Debye relaxation is identified for the slowest dynamics, differing from the dielectric spectra of short-chain alcohols. The broadening of the Debye-like relaxation in the two liquids develops as temperature increases, and the approaching of the Debye-like and structural relaxation widths is shown. Similar results are observed in the dielectric spectra of dilute 2-ethyl-1-hexanol in either 2-hexyl-1-decanol or squalane. The results of the liquids and mixtures reveal a correlation between the broadening and the Debye-like relaxation strength. Molecular associations in monohydroxy alcohols are discussed with the modification of the Debye relaxation.
SYNTHESIZED EXPECTED BAYESIAN METHOD OF PARAMETRIC ESTIMATE
Institute of Scientific and Technical Information of China (English)
Ming HAN; Yuanyao DING
2004-01-01
This paper develops a new method of parametric estimate, which is named as "synthesized expected Bayesian method". When samples of products are tested and no failure events occur, thedefinition of expected Bayesian estimate is introduced and the estimates of failure probability and failure rate are provided. After some failure information is introduced by making an extra-test, a synthesized expected Bayesian method is defined and used to estimate failure probability, failure rateand some other parameters in exponential distribution and Weibull distribution of populations. Finally,calculations are performed according to practical problems, which show that the synthesized expected Bayesian method is feasible and easy to operate.
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....
Variational bayesian method of estimating variance components.
Arakawa, Aisaku; Taniguchi, Masaaki; Hayashi, Takeshi; Mikawa, Satoshi
2016-07-01
We developed a Bayesian analysis approach by using a variational inference method, a so-called variational Bayesian method, to determine the posterior distributions of variance components. This variational Bayesian method and an alternative Bayesian method using Gibbs sampling were compared in estimating genetic and residual variance components from both simulated data and publically available real pig data. In the simulated data set, we observed strong bias toward overestimation of genetic variance for the variational Bayesian method in the case of low heritability and low population size, and less bias was detected with larger population sizes in both methods examined. The differences in the estimates of variance components between the variational Bayesian and the Gibbs sampling were not found in the real pig data. However, the posterior distributions of the variance components obtained with the variational Bayesian method had shorter tails than those obtained with the Gibbs sampling. Consequently, the posterior standard deviations of the genetic and residual variances of the variational Bayesian method were lower than those of the method using Gibbs sampling. The computing time required was much shorter with the variational Bayesian method than with the method using Gibbs sampling.
Statistical mechanics of violent relaxation
Spergel, David N.; Hernquist, Lars
1992-01-01
We propose a functional that is extremized through violent relaxation. It is based on the Ansatz that the wave-particle scattering during violent dynamical processes can be approximated as a sequence of discrete scattering events that occur near a particle's perigalacticon. This functional has an extremum whose structure closely resembles that of spheroidal stellar systems such as elliptical galaxies. The results described here, therefore, provide a simple framework for understanding the physical nature of violent relaxation and support the view that galaxies are structured in accord with fundamental statistical principles.
LAVENDER AROMATERAPHY AS A RELAXANT
Directory of Open Access Journals (Sweden)
IGA Prima Dewi AP
2013-02-01
Full Text Available Aromatherapy is a kind of treatment that used aroma with aromatherapy essential oil. Extraction process from essential oil generally doing in three methods, there are distilling with water (boiled, distilling with water and steam, and distilling with steam. One of the most favorite aroma is lavender. The main content from lavender is linalyl acetate and linalool (C10H18O. Linalool is main active contents in lavender which can use for anti-anxiety (relaxation. Based on some research, the conclusion indicates that essential oil from lavender can give relaxation (carminative, sedative, reduce anxiety level and increasing mood.
Electron spin relaxation in cryptochrome-based magnetoreception
DEFF Research Database (Denmark)
Kattnig, Daniel R; Solov'yov, Ilia A; Hore, P J
2016-01-01
The magnetic compass sense of migratory birds is thought to rely on magnetically sensitive radical pairs formed photochemically in cryptochrome proteins in the retina. An important requirement of this hypothesis is that electron spin relaxation is slow enough for the Earth's magnetic field to have...... a significant effect on the coherent spin dynamics of the radicals. It is generally assumed that evolutionary pressure has led to protection of the electron spins from irreversible loss of coherence in order that the underlying quantum dynamics can survive in a noisy biological environment. Here, we address...... this question for a structurally characterized model cryptochrome expected to share many properties with the putative avian receptor protein. To this end we combine all-atom molecular dynamics simulations, Bloch-Redfield relaxation theory and spin dynamics calculations to assess the effects of spin relaxation...
Rheology and Relaxation Timescales of ABA Triblock Polymer Gels
Peters, Andrew; Lodge, Timothy
When dissolved in a midblock selective solvent, ABA polymers form gels composed of aggregated end block micelles bridged by the midblocks. While much effort has been devoted to the study of the structure of these systems, the dynamics of these systems has received less attention. We examine the underlying mechanism of shear relaxation of ABA triblock polymer gels, especially as a function of chain length, composition, and concentration. Recent work using time-resolved small-angle neutron scattering of polystyrene (PS)-block-poly(ethylene-alt-propylene) (PEP) in squalane has elucidated many aspects of the dynamics of diblock chain exchange. By using rheology to study bulk relaxation phenomena of the triblock equivalent, PS-PEP-PS, we apply the knowledge gained from the chain exchange studies to bridge the gap between the molecular and macroscopic relaxation phenomena in PS-PEP-PS triblock gels.
Dielectric relaxation of CdO nanoparticles
Tripathi, Ramna; Dutta, Alo; Das, Sayantani; Kumar, Akhilesh; Sinha, T. P.
2016-02-01
Nanoparticles of cadmium oxide have been synthesized by soft chemical route using thioglycerol as the capping agent. The crystallite size is determined by X-ray diffraction technique and the particle size is obtained by transmission electron microscope. The band gap of the material is obtained using Tauc relation to UV-visible absorption spectrum. The photoluminescence emission spectra of the sample are measured at various excitation wavelengths. The molecular components in the material have been analyzed by FT-IR spectroscopy. The dielectric dispersion of the material is investigated in the temperature range from 313 to 393 K and in the frequency range from 100 Hz to 1 MHz by impedance spectroscopy. The Cole-Cole model is used to describe the dielectric relaxation of the system. The scaling behavior of imaginary part of impedance shows that the relaxation describes the same mechanism at various temperatures. The frequency-dependent electrical data are also analyzed in the framework of conductivity and electrical modulus formalisms. The frequency-dependent conductivity spectra are found to obey the power law.
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.
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
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 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 homeopathy: talking normal again.
Rutten, A L B
2007-04-01
Homeopathy has a communication problem: important homeopathic concepts are not understood by conventional colleagues. Homeopathic terminology seems to be comprehensible only after practical experience of homeopathy. The main problem lies in different handling of diagnosis. In conventional medicine diagnosis is the starting point for randomised controlled trials to determine the effect of treatment. In homeopathy diagnosis is combined with other symptoms and personal traits of the patient to guide treatment and predict response. Broadening our scope to include diagnostic as well as treatment research opens the possibility of multi factorial reasoning. Adopting Bayesian methodology opens the possibility of investigating homeopathy in everyday practice and of describing some aspects of homeopathy in conventional terms.
Sastry, V. S. S.; Polimeno, Antonino; Crepeau, Richard H.; Freed, Jack H.
1996-10-01
Two-dimensional Fourier transform (2D-FT) electron spin resonance (ESR) studies on the rigid rodlike cholestane (CSL) spin-label in the liquid crystal solvent 4O,8 (butoxy benzylidene octylaniline) are reported. These experiments were performed over a wide temperature range: 96 °C to 25 °C covering the isotropic (I), nematic (N), smectic A (SA), smectic B (SB), and crystal (C) phases. It is shown that 2D-FT-ESR, especially in the form of 2D-ELDOR (two-dimensional electron-electron double resonance) provides greatly enhanced sensitivity to rotational dynamics than previous cw-ESR studies on this and related systems. This sensitivity is enhanced by obtaining a series of 2D-ELDOR spectra as a function of mixing time, Tm, yielding essentially a three-dimensional experiment. Advantage is taken of this sensitivity to study the applicability of the model of a slowly relaxing local structure (SRLS). In this model, a dynamic cage of solvent molecules, which relaxes on a slower time scale than the CSL solute, provides a local orienting potential in addition to that of the macroscopic aligning potential in the liquid crystalline phase. The theory of Polimeno and Freed for SRLS in the ESR slow motional regime is extended by utilizing the theory of Lee et al. to include 2D-FT-ESR experiments, and it serves as the basis for the analysis of the 2D-ELDOR experiments. It is shown that the SRLS model leads to significantly improved non-linear least squares fits to experiment over those obtained with the standard model of Brownian reorientation in a macroscopic aligning potential. This is most evident for the SA phase, and the use of the SRLS model also removes the necessity of fitting with the unreasonably large CSL rotational asymmetries in the smectic phases that are required in both the cw-ESR and 2D-ELDOR fits with the standard model. The cage potential is found to vary from about kBT in the isotropic phase to greater than 2kBT in the N and SA phases, with an abrupt drop to
Relaxation and resonances in fluctuating dielectric systems
Garcia-Colin, L. S.; del Castillo, L. F.
1989-09-01
In this paper we show how the ideas behind extended irreversible thermodynamics are used to generate a systematic treatment of the relaxation and resonance phenomena in the propagation and absorption of electromagnetic energy in dielectric materials in a nonequilibrium state. Two cases are discussed: the first, in which the forced oscillations arising from the correlation between the fluctuations of the polarization vector and the electric field are neglected, and the second, in which this term is taken into account. In both cases we show how the main equations serve to make a connection between the macroscopic approach followed here and a number of results obtained for both, gases and polar liquids using molecular models. The results obtained here are compared with previous work on this problem, and new effects arising from the second case are pointed out.
Directory of Open Access Journals (Sweden)
M. Sadegh
2013-04-01
Full Text Available In recent years, a strong debate has emerged in the hydrologic literature how to properly treat non-traditional error residual distributions and quantify parameter and predictive uncertainty. Particularly, there is strong disagreement whether such uncertainty framework should have its roots within a proper statistical (Bayesian context using Markov chain Monte Carlo (MCMC simulation techniques, or whether such a framework should be based on a quite different philosophy and implement informal likelihood functions and simplistic search methods to summarize parameter and predictive distributions. In this paper we introduce an alternative framework, called Approximate Bayesian Computation (ABC that summarizes the differing viewpoints of formal and informal Bayesian approaches. This methodology has recently emerged in the fields of biology and population genetics and relaxes the need for an explicit likelihood function in favor of one or multiple different summary statistics that measure the distance of each model simulation to the data. This paper is a follow up of the recent publication of Nott et al. (2012 and further studies the theoretical and numerical equivalence of formal (DREAM and informal (GLUE Bayesian approaches using data from different watersheds in the United States. We demonstrate that the limits of acceptability approach of GLUE is a special variant of ABC in which each discharge observation of the calibration data set is used as a summary diagnostic.
Relaxation properties in classical diamagnetism
Carati, A.; Benfenati, F.; Galgani, L.
2011-06-01
It is an old result of Bohr that, according to classical statistical mechanics, at equilibrium a system of electrons in a static magnetic field presents no magnetization. Thus a magnetization can occur only in an out of equilibrium state, such as that produced through the Foucault currents when a magnetic field is switched on. It was suggested by Bohr that, after the establishment of such a nonequilibrium state, the system of electrons would quickly relax back to equilibrium. In the present paper, we study numerically the relaxation to equilibrium in a modified Bohr model, which is mathematically equivalent to a billiard with obstacles, immersed in a magnetic field that is adiabatically switched on. We show that it is not guaranteed that equilibrium is attained within the typical time scales of microscopic dynamics. Depending on the values of the parameters, one has a relaxation either to equilibrium or to a diamagnetic (presumably metastable) state. The analogy with the relaxation properties in the Fermi Pasta Ulam problem is also pointed out.
Dielectric relaxation of samarium aluminate
Energy Technology Data Exchange (ETDEWEB)
Sakhya, Anup Pradhan; Dutta, Alo; Sinha, T.P. [Bose Institute, Department of Physics, Kolkata (India)
2014-03-15
A ceramic SmAlO{sub 3} (SAO) sample is synthesized by the solid-state reaction technique. The Rietveld refinement of the X-ray diffraction pattern has been done to find the crystal symmetry of the sample at room temperature. An impedance spectroscopy study of the sample has been performed in the frequency range from 50 Hz to 1 MHz and in the temperature range from 313 K to 573 K. Dielectric relaxation peaks are observed in the imaginary parts of the spectra. The Cole-Cole model is used to analyze the dielectric relaxation mechanism in SAO. The temperature-dependent relaxation times are found to obey the Arrhenius law having an activation energy of 0.29 eV, which indicates that polaron hopping is responsible for conduction or dielectric relaxation in this material. The complex impedance plane plot of the sample indicates the presence of both grain and grain-boundary effects and is analyzed by an electrical equivalent circuit consisting of a resistance and a constant-phase element. The frequency-dependent conductivity spectra follow a double-power law due to the presence of two plateaus. (orig.)
Onsager relaxation of toroidal plasmas
Energy Technology Data Exchange (ETDEWEB)
Samain, A.; Nguyen, F.
1997-01-01
The slow relaxation of isolated toroidal plasmas towards their thermodynamical equilibrium is studied in an Onsager framework based on the entropy metric. The basic tool is a variational principle, equivalent to the kinetic equation, involving the profiles of density, temperature, electric potential, electric current. New minimization procedures are proposed to obtain entropy and entropy production rate functionals. (author). 36 refs.
Choosing a skeletal muscle relaxant.
See, Sharon; Ginzburg, Regina
2008-08-01
Skeletal muscle relaxants are widely used in treating musculoskeletal conditions. However, evidence of their effectiveness consists mainly of studies with poor methodologic design. In addition, these drugs have not been proven to be superior to acetaminophen or nonsteroidal anti-inflammatory drugs for low back pain. Systematic reviews and meta-analyses support using skeletal muscle relaxants for short-term relief of acute low back pain when nonsteroidal anti-inflammatory drugs or acetaminophen are not effective or tolerated. Comparison studies have not shown one skeletal muscle relaxant to be superior to another. Cyclobenzaprine is the most heavily studied and has been shown to be effective for various musculoskeletal conditions. The sedative properties of tizanidine and cyclobenzaprine may benefit patients with insomnia caused by severe muscle spasms. Methocarbamol and metaxalone are less sedating, although effectiveness evidence is limited. Adverse effects, particularly dizziness and drowsiness, are consistently reported with all skeletal muscle relaxants. The potential adverse effects should be communicated clearly to the patient. Because of limited comparable effectiveness data, choice of agent should be based on side-effect profile, patient preference, abuse potential, and possible drug interactions.
Sastry, V. S. S.; Polimeno, Antonino; Crepeau, Richard H.; Freed, Jack H.
1996-10-01
Two-dimensional Fourier transform (2D-FT)-electron spin resonance (ESR) studies on the small globular spin probe perdeuterated tempone (PDT) in the liquid crystal solvent 4O,8 (butoxy benzylidene octylaniline) are reported. These experiments, over the temperature range of 95 °C to 24 °C, cover the isotropic (I), nematic (N), smectic A (SA), smectic B (SB), and crystal (C) phases. The 2D-ELDOR (two-dimensional electron-electron double resonance) spectra confirm the anomalously rapid reorientation of PDT, especially in the lower temperature phases. The model of a slowly relaxing local structure (SRLS) leads to generally very good non-linear least squares (NLLS) global fits to the sets of 2D-ELDOR spectra obtained at each temperature. These fits are significantly better than those achieved by the standard model of Brownian reorientation in a macroscopic orienting potential. The SRLS model is able to account for anomalies first observed in an earlier 2D-ELDOR study on PDT in a different liquid crystal in its smectic phases. Although it is instructional to extract the various spectral densities from the COSY (correlation spectroscopy) and 2D-ELDOR spectra, the use of NLLS global fitting to a full set of 2D-ELDOR spectra is shown to be more reliable and convenient for obtaining optimum model parameters, especially in view of possible (incipient) slow motional effects from the SRLS or dynamic cage. The cage potential is found to remain fairly constant at about kBT over the various phases (with only a small drop in the SB phase), but its asymmetry increases with decreasing temperature T. This value is significantly larger than the weak macroscopic orienting potential which increases from 0.1 to 0.3kBT with decreasing T. The cage relaxation rate, given by Rc is about 3×107 s-1 in the I phase, but increases to about 108 s-1 in the SA, SB, and C phases. The rotational diffusion tensor for PDT shows only a small T-independent asymmetry, and its mean rotational diffusion
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.
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
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....
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
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…
The Bayesian Revolution Approaches Psychological Development
Shultz, Thomas R.
2007-01-01
This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…
Bayesian analysis of exoplanet and binary orbits
Schulze-Hartung, Tim; Launhardt, Ralf; 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.
Bayesian Network for multiple hypthesis tracking
W.P. Zajdel; B.J.A. Kröse
2002-01-01
For a flexible camera-to-camera tracking of multiple objects we model the objects behavior with a Bayesian network and combine it with the multiple hypohesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a produ
DEFF Research Database (Denmark)
Guillot, Gilles; Carpentier-Skandalis, Alexandra
2011-01-01
We study the accuracy of a Bayesian supervised method used to cluster individuals into genetically homogeneous groups on the basis of dominant or codominant molecular markers. We provide a formula relating an error criterion to the number of loci used and the number of clusters. This formula...
Hepatitis disease detection using Bayesian theory
Maseleno, Andino; Hidayati, Rohmah Zahroh
2017-02-01
This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.
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 ...
Meredith, Robert W; Pires, Marcelo N; Reznick, David N; Springer, Mark S
2010-05-01
Poeciliids are one of the most intensively studied groups within Cyprinodontiformes owing to their use as model organisms for experimental studies on natural and sexual selection, and comparative studies of life-history evolution. Life-history studies have demonstrated multiple origins of placentotrophy and superfetation in poeciliids, including the recent description of placentotrophy in three species of Poecilia (Micropoecilia): P. bifurca, P. branneri, and P. parae. Here, we use a concatenation of seven nuclear gene segments and two mitochondrial segments to examine relationships within Micropoecilia and between this subgenus and other subgenera in Poecilia (Mollienesia, Limia, Pamphorichthys, Acanthophacelus). The combined molecular data set (8668 bp) was analyzed with maximum parsimony, maximum likelihood, and Bayesian methods. We also employed a relaxed molecular clock method to estimate divergence times within Poecilia. All phylogenetic analyses with the combined DNA data set supported the monophyly of Poecilia and recovered a basal split between Poecilia (Acanthophacelus)+Poecilia (Micropoecilia) and the other three subgenera. Within Micropoecilia, P. bifurca grouped with P. branneri, and these joined P. parae to the exclusion of P. picta. Ancestral reconstructions based on parsimony and Bayesian methods suggest that placentotrophy evolved once in Micropoecilia in the common ancestor of P. bifurca, P. branneri, and P. parae. Divergence time estimates suggest that placentotrophy in Micropoecilia evolved in 4 million years.
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.
Approximate Bayesian Computation in Large Scale Structure: constraining the galaxy-halo connection
Hahn, ChangHoon; Walsh, Kilian; Hearin, Andrew P; Hogg, David W; Cambpell, Duncan
2016-01-01
The standard approaches to Bayesian parameter inference in large scale structure (LSS) assume a Gaussian functional form (chi-squared form) for the likelihood. They are also typically restricted to measurements such as the two point correlation function. Likelihood free inferences such as Approximate Bayesian Computation (ABC) make inference possible without assuming any functional form for the likelihood, thereby relaxing the assumptions and restrictions of the standard approach. Instead it relies on a forward generative model of the data and a metric for measuring the distance between the model and data. In this work, we demonstrate that ABC is feasible for LSS parameter inference by using it to constrain parameters of the halo occupation distribution (HOD) model for populating dark matter halos with galaxies. Using specific implementation of ABC supplemented with Population Monte Carlo importance sampling, a generative forward model using HOD, and a distance metric based on galaxy number density, two-point...
Equivalent Relaxations of Optimal Power Flow
Energy Technology Data Exchange (ETDEWEB)
Bose, S; Low, SH; Teeraratkul, T; Hassibi, B
2015-03-01
Several convex relaxations of the optimal power flow (OPF) problem have recently been developed using both bus injection models and branch flow models. In this paper, we prove relations among three convex relaxations: a semidefinite relaxation that computes a full matrix, a chordal relaxation based on a chordal extension of the network graph, and a second-order cone relaxation that computes the smallest partial matrix. We prove a bijection between the feasible sets of the OPF in the bus injection model and the branch flow model, establishing the equivalence of these two models and their second-order cone relaxations. Our results imply that, for radial networks, all these relaxations are equivalent and one should always solve the second-order cone relaxation. For mesh networks, the semidefinite relaxation and the chordal relaxation are equally tight and both are strictly tighter than the second-order cone relaxation. Therefore, for mesh networks, one should either solve the chordal relaxation or the SOCP relaxation, trading off tightness and the required computational effort. Simulations are used to illustrate these results.
Plasmon-mediated energy relaxation in graphene
Energy Technology Data Exchange (ETDEWEB)
Ferry, D. K. [School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287-5706 (United States); Somphonsane, R. [Department of Physics, King Mongkut' s Institute of Technology, Ladkrabang, Bangkok 10520 (Thailand); Ramamoorthy, H.; Bird, J. P. [Department of Electrical Engineering, University at Buffalo, the State University of New York, Buffalo, New York 14260-1500 (United States)
2015-12-28
Energy relaxation of hot carriers in graphene is studied at low temperatures, where the loss rate may differ significantly from that predicted for electron-phonon interactions. We show here that plasmons, important in the relaxation of energetic carriers in bulk semiconductors, can also provide a pathway for energy relaxation in transport experiments in graphene. We obtain a total loss rate to plasmons that results in energy relaxation times whose dependence on temperature and density closely matches that found experimentally.
INVESTIGATION ON NONEQULIBRIUM RADIATION AND RELAXATION PHENOMENA IN SHOCK TUBES
Institute of Scientific and Technical Information of China (English)
竺乃宜; 杨乾锁; 张恒利; 余西龙; 黄立舜
2003-01-01
The experimental results for the excited time of the nonequlibrium radiation andthe ionization behind strong shock waves are presented. Using an optical multichannel analyzer, InSb infrared detectors and near-free-molecular Langmuir probes, the infrared radiation, the electron density of air and the nonequlibrium radiation spectra at different moments of the relaxation process in nitrogen test gas behind normal shock waves were obtained, respectively, in hydrogen oxygen combustion driven shock tubes.
Identification of Structural Relaxation in the Dielectric Response of Water
Hansen, Jesper S.; Kisliuk, Alexander; Sokolov, Alexei P.; Gainaru, Catalin
2016-06-01
One century ago pioneering dielectric results obtained for water and n -alcohols triggered the advent of molecular rotation diffusion theory considered by Debye to describe the primary dielectric absorption in these liquids. Comparing dielectric, viscoelastic, and light scattering results, we unambiguously demonstrate that the structural relaxation appears only as a high-frequency shoulder in the dielectric spectra of water. In contrast, the main dielectric peak is related to a supramolecular structure, analogous to the Debye-like peak observed in monoalcohols.
Collisionless Relaxation of Stellar Systems
Kandrup, H E
1998-01-01
The objective of the work summarised here has been to exploit and extend ideas from plasma physics and accelerator dynamics to formulate a unified description of collisionless relaxation that views violent relaxation, Landau damping, and phase mixing as (manifestations of) a single phenomenon. This approach embraces the fact that the collisionless Boltzmann equation (CBE), the basic object of the theory, is an infinite-dimensional Hamiltonian system, with the distribution function f playing the role of the fundamental dynamical variable, and that, interpreted appropriately, an evolution described by the other Hamiltonian system. Equilibrium solutions correspond to extremal points of the Hamiltonian subject to the constraints associated with Liouville's Theorem. Stable equilibria correspond to energy minima. The evolution of a system out of equilibrium involves (in general nonlinear) phase space oscillations which may -- or may not -- interfere destructively so as to damp away.
Collisionless Relaxation of Stellar Systems
Kandrup, Henry E.
1999-08-01
The objective of the work summarized here has been to exploit and extend ideas from plasma physics and accelerator dynamics to formulate a unified description of collisionless relaxation of stellar systems that views violent relaxation, Landau damping, and phase mixing as (manifestations of) a single phenomenon. This approach embraces the fact that the collisionless Boltzmann equation (CBE), the basic object of the theory, is an infinite-dimensional Hamiltonian system, with the distribution function f playing the role of the fundamental dynamical variable, and that, interpreted appropriately, an evolution described by the CBE is no different fundamentally from an evolution described by any other Hamiltonian system. Equilibrium solutions f0 correspond to extremal points of the Hamiltonian subject to the constraints associated with Liouville's Theorem. Stable equilibria correspond to energy minima. The evolution of a system out of equilibrium involves (in general nonlinear) phase space oscillations which may - or may not - interfere destructively so as to damp away.
Kinetic activation-relaxation technique
Béland, Laurent Karim; Brommer, Peter; El-Mellouhi, Fedwa; Joly, Jean-François; Mousseau, Normand
2011-10-01
We present a detailed description of the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe, and structural relaxation in a-Si (amorphous silicon).
Kinetic activation-relaxation technique.
Béland, Laurent Karim; Brommer, Peter; El-Mellouhi, Fedwa; Joly, Jean-François; Mousseau, Normand
2011-10-01
We present a detailed description of the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe, and structural relaxation in a-Si (amorphous silicon).
Arresting relaxation in Pickering Emulsions
Atherton, Tim; Burke, Chris
2015-03-01
Pickering emulsions consist of droplets of one fluid dispersed in a host fluid and stabilized by colloidal particles absorbed at the fluid-fluid interface. Everyday materials such as crude oil and food products like salad dressing are examples of these materials. Particles can stabilize non spherical droplet shapes in these emulsions through the following sequence: first, an isolated droplet is deformed, e.g. by an electric field, increasing the surface area above the equilibrium value; additional particles are then adsorbed to the interface reducing the surface tension. The droplet is then allowed to relax toward a sphere. If more particles were adsorbed than can be accommodated by the surface area of the spherical ground state, relaxation of the droplet is arrested at some non-spherical shape. Because the energetic cost of removing adsorbed colloids exceeds the interfacial driving force, these configurations can remain stable over long timescales. In this presentation, we present a computational study of the ordering present in anisotropic droplets produced through the mechanism of arrested relaxation and discuss the interplay between the geometry of the droplet, the dynamical process that produced it, and the structure of the defects observed.
Chang, Zhiwei; Halle, Bertil
2013-10-01
In complex biological or colloidal samples, magnetic relaxation dispersion (MRD) experiments using the field-cycling technique can characterize molecular motions on time scales ranging from nanoseconds to microseconds, provided that a rigorous theory of nuclear spin relaxation is available. In gels, cross-linked proteins, and biological tissues, where an immobilized macromolecular component coexists with a mobile solvent phase, nuclear spins residing in solvent (or cosolvent) species relax predominantly via exchange-mediated orientational randomization (EMOR) of anisotropic nuclear (electric quadrupole or magnetic dipole) couplings. The physical or chemical exchange processes that dominate the MRD typically occur on a time scale of microseconds or longer, where the conventional perturbation theory of spin relaxation breaks down. There is thus a need for a more general relaxation theory. Such a theory, based on the stochastic Liouville equation (SLE) for the EMOR mechanism, is available for a single quadrupolar spin I = 1. Here, we present the corresponding theory for a dipole-coupled spin-1/2 pair. To our knowledge, this is the first treatment of dipolar MRD outside the motional-narrowing regime. Based on an analytical solution of the spatial part of the SLE, we show how the integral longitudinal relaxation rate can be computed efficiently. Both like and unlike spins, with selective or non-selective excitation, are treated. For the experimentally important dilute regime, where only a small fraction of the spin pairs are immobilized, we obtain simple analytical expressions for the auto-relaxation and cross-relaxation rates which generalize the well-known Solomon equations. These generalized results will be useful in biophysical studies, e.g., of intermittent protein dynamics. In addition, they represent a first step towards a rigorous theory of water 1H relaxation in biological tissues, which is a prerequisite for unravelling the molecular basis of soft
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
Energy Technology Data Exchange (ETDEWEB)
Soerensen Ringi, M.
1995-05-01
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.
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...
Bayesian Inference with Optimal Maps
Moselhy, Tarek A El
2011-01-01
We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selectio...
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
Correlated component analysis as proposed by Dmochowski, Sajda, Dias, and Parra (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are iden......Correlated component analysis as proposed by Dmochowski, Sajda, Dias, and Parra (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
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 ...
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...
Bayesian Inference in Queueing Networks
Sutton, Charles
2010-01-01
Modern Web services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where one queue models each of the individual computers in the system. A key challenge is that the data is incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model...
A Bayesian Reflection on Surfaces
Directory of Open Access Journals (Sweden)
David R. Wolf
1999-10-01
Full Text Available Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data, is presented.
Institute of Scientific and Technical Information of China (English)
王卫东; 郝跃; 纪翔; 易成龙; 牛翔宇
2012-01-01
分别采用Tersoff-Brenner势和AIREBO势，对三种长宽比的单层石墨烯纳米带在不同热力学温度（0．01—4000K）下的弛豫性能进行了分子动力学模拟．对基于两种势函数模拟的石墨烯纳米带弛豫的能量曲线和表面形貌进行了分析对比，研究了石墨烯纳米带在弛豫过程中的动态平衡过程．模拟结果表明：单层石墨烯纳米带并非完美的平面结构，边缘处和内部都会呈现一定程度的起伏和皱褶，这与已有的实验结果相符合；石墨烯纳米带的表面起伏程度随长宽比的减小而减小，并且在不同温度条件下，系统动能对石墨烯纳米带的弛豫变形的影响很大，即系统温度越高，石墨烯纳米带的弛豫变形幅度愈大；高长宽比纳米带在一定温度条件下甚至会出现卷曲现象．最后，对采用Tersoff-Brenner势和AIREBO势进行石墨烯的分子动力学模拟进行了深入分析．%At different thermodynamic temperatures (between 0.01 and 4000 K), the relaxation properties of three kinds of graphene nanorib- bons with different aspect ratios are simulated by molecular dynamics method based on Tersoff-Brenner and AIREBO potential func- tions separately. Then we compare the energy curves and surface morphologies of nanoribbon relaxation with two kinds of potential functions, and study the dynamic equilibrium process of the graphene nanoribbons during their relaxation simulation. The simulation results show that the single layer graphene nanoribbon is not of a perfect planar structure and that a certain degree of fluctuations and folds occur at the edges and inside of nanoribbons, which are consistent with the existing experimental results; the surface fluctuation level of graphene nanoribbons decreases with the reduction of the aspect ratio, and the system kinetic energy has a dramatic influence on the relaxation deformation of the graphene nanoribbons at different temperatures, which
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
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
2006-01-01
We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...... by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Short-Time Beta Relaxation in Glass-Forming Liquids Is Cooperative in Nature
Karmakar, Smarajit; Dasgupta, Chandan; Sastry, Srikanth
2016-02-01
Temporal relaxation of density fluctuations in supercooled liquids near the glass transition occurs in multiple steps. Using molecular dynamics simulations for three model glass-forming liquids, we show that the short-time β relaxation is cooperative in nature. Using finite-size scaling analysis, we extract a growing length scale associated with beta relaxation from the observed dependence of the beta relaxation time on the system size. We find, in qualitative agreement with the prediction of the inhomogeneous mode coupling theory, that the temperature dependence of this length scale is the same as that of the length scale that describes the spatial heterogeneity of local dynamics in the long-time α -relaxation regime.
Time of relaxation in dusty plasma model
Timofeev, A. V.
2015-11-01
Dust particles in plasma may have different values of average kinetic energy for vertical and horizontal motion. The partial equilibrium of the subsystems and the relaxation processes leading to this asymmetry are under consideration. A method for the relaxation time estimation in nonideal dusty plasma is suggested. The characteristic relaxation times of vertical and horizontal motion of dust particles in gas discharge are estimated by analytical approach and by analysis of simulation results. These relaxation times for vertical and horizontal subsystems appear to be different. A single hierarchy of relaxation times is proposed.
Shetty, Rahul; Bigiel, Frank
2012-01-01
We develop a Bayesian linear regression method which rigorously treats measurement uncertainties, and accounts for hierarchical data structure for investigating the relationship between the star formation rate and gas surface density. The method simultaneously estimates the intercept, slope, and scatter about the regression line of each individual subject (e.g. a galaxy) and the population (e.g. an ensemble of galaxies). Using synthetic datasets, we demonstrate that the Bayesian method accurately recovers the parameters of both the individuals and the population, especially when compared to commonly employed least squares methods, such as the bisector. We apply the Bayesian method to estimate the Kennicutt-Schmidt (KS) parameters of a sample of spiral galaxies compiled by Bigiel et al. (2008). We find significant variation in the KS parameters, indicating that no single KS relationship holds for all galaxies. This suggests that the relationship between molecular gas and star formation differs between galaxies...
Directory of Open Access Journals (Sweden)
Ali Ahmed
2011-01-01
Full Text Available Problem statement: Similarity based Virtual Screening (VS deals with a large amount of data containing irrelevant and/or redundant fragments or features. Recent use of Bayesian network as an alternative for existing tools for similarity based VS has received noticeable attention of the researchers in the field of chemoinformatics. Approach: To this end, different models of Bayesian network have been developed. In this study, we enhance the Bayesian Inference Network (BIN using a subset of selected molecules features. Results: In this approach, a few features were filtered from the molecular fingerprint features based on a features selection approach. Conclusion: Simulated virtual screening experiments with MDL Drug Data Report (MDDR data sets showed that the proposed method provides simple ways of enhancing the cost effectiveness of ligand-based virtual screening searches, especially for higher diversity data set.
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.
Fast Heterogeneous Relaxation Near The Glass Transition
Russina, Margarita
2000-03-01
More than a decade ago inelastic neutron scattering studies revealed a surprising characteristic feature in the atomic dynamics near the glass transition, which was often called the betta-process, with reference to predictions of the mode coupling theory (MCT). This process appears on the ps time scale, i.e. fast compared to the ordinary flow viscosity governed relaxation and slow compared to usual atomic vibrations, and its nature remained a puzzle over the years. Although inelastic neutron scattering is ideally suited to observe dynamics on microscopic time and length scales, experimental difficulties due to strong multiple scattering effects prevented the exploration of the spatial character of this process. By a new experimental approach to correct for these spurious contributions with a high precision, we were now able to extend the spatial domain of our observations from just about nearest neighbor atomic distances by close to an order of magnitude larger ones, which length scale includes that of the intermediate range order, which can be expected to reveal most sensitively collective, as opposed to the local, behavior. Our results in the fragile glass forming liquid Ca-K-NO3 show, that the betta-process is a first fast step of the structural relaxation, which confirms a most fundamental prediction of MCT. Furthermore, by investigating the Debye-Waller factor associated with this process, we found that its geometrical nature corresponds to quasi-rigid, correlated displacement of mobile groups of atoms, which move much faster than the ordinary flow of the bulk of the supercooled liquid. This is the first direct experimental evidence for the existence of heterogeneous fast flow processes similar to the string-flow motion recently observed in molecular dynamic simulations of model liquids close to the glass transition.
On the reliability of NMR relaxation data analyses: a Markov Chain Monte Carlo approach.
Abergel, Daniel; Volpato, Andrea; Coutant, Eloi P; Polimeno, Antonino
2014-09-01
The analysis of NMR relaxation data is revisited along the lines of a Bayesian approach. Using a Markov Chain Monte Carlo strategy of data fitting, we investigate conditions under which relaxation data can be effectively interpreted in terms of internal dynamics. The limitations to the extraction of kinetic parameters that characterize internal dynamics are analyzed, and we show that extracting characteristic time scales shorter than a few tens of ps is very unlikely. However, using MCMC methods, reliable estimates of the marginal probability distributions and estimators (average, standard deviations, etc.) can still be obtained for subsets of the model parameters. Thus, unlike more conventional strategies of data analysis, the method avoids a model selection process. In addition, it indicates what information may be extracted from the data, but also what cannot.
GPstuff: Bayesian Modeling with Gaussian Processes
Vanhatalo, J.; Riihimaki, J.; Hartikainen, J.; Jylänki, P.P.; Tolvanen, V.; Vehtari, A.
2013-01-01
The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.
Bayesian Uncertainty Analyses Via Deterministic Model
Krzysztofowicz, R.
2001-05-01
Rational decision-making requires that the total uncertainty about a variate of interest (a predictand) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Suppose the state-of-knowledge is embodied in a deterministic model, which is imperfect and outputs only an estimate of the predictand. Fundamentals are presented of three Bayesian approaches to producing a probability distribution of the predictand via any deterministic model. The Bayesian Processor of Output (BPO) quantifies the total uncertainty in terms of a posterior distribution, conditional on model output. The Bayesian Processor of Ensemble (BPE) quantifies the total uncertainty in terms of a posterior distribution, conditional on an ensemble of model output. The Bayesian Forecasting System (BFS) decomposes the total uncertainty into input uncertainty and model uncertainty, which are characterized independently and then integrated into a predictive distribution.
Picturing classical and quantum Bayesian inference
Coecke, Bob
2011-01-01
We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect to an appropriate compact structure. We characterize classical Bayesian inference in terms of a graphical property and demonstrate that our approach eliminates some purely conventional elements that appear in common representations thereof, such as whether degrees of belief are represented by probabilities or entropic quantities. We also introduce a quantum-like calculus wherein the Frobenius structure is noncommutative and show that it can accommodate Leifer's calculus of `cond...
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.
An Intuitive Dashboard for Bayesian Network Inference
Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.
2014-03-01
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.
Energy Technology Data Exchange (ETDEWEB)
Pravdivtsev, Andrey N.; Yurkovskaya, Alexandra V.; Ivanov, Konstantin L., E-mail: ivanov@tomo.nsc.ru [International Tomography Center, Institutskaya 3a, Novosibirsk 630090 (Russian Federation); Department of Physics, Novosibirsk State University, Pirogova 2, Novosibirsk 630090 (Russian Federation); Vieth, Hans-Martin [Institut für Experimentalphysik, Freie Universität Berlin Arnimallee 14, 14195 Berlin (Germany)
2014-10-21
Nuclear Magnetic Relaxation Dispersion (NMRD) of protons was studied in the pentapeptide Met-enkephalin and the amino acids, which constitute it. Experiments were run by using high-resolution Nuclear Magnetic Resonance (NMR) in combination with fast field-cycling, thus enabling measuring NMRD curves for all individual protons. As in earlier works, Papers I–III, pronounced effects of intramolecular scalar spin-spin interactions, J-couplings, on spin relaxation were found. Notably, at low fields J-couplings tend to equalize the apparent relaxation rates within networks of coupled protons. In Met-enkephalin, in contrast to the free amino acids, there is a sharp increase in the proton T{sub 1}-relaxation times at high fields due to the changes in the regime of molecular motion. The experimental data are in good agreement with theory. From modelling the relaxation experiments we were able to determine motional correlation times of different residues in Met-enkephalin with atomic resolution. This allows us to draw conclusions about preferential conformation of the pentapeptide in solution, which is also in agreement with data from two-dimensional NMR experiments (rotating frame Overhauser effect spectroscopy). Altogether, our study demonstrates that high-resolution NMR studies of magnetic field-dependent relaxation allow one to probe molecular mobility in biomolecules with atomic resolution.
ProFit: Bayesian galaxy fitting tool
Robotham, A. S. G.; Taranu, D.; Tobar, R.
2016-12-01
ProFit is a Bayesian galaxy fitting tool that uses the fast C++ image generation library libprofit (ascl:1612.003) and a flexible R interface to a large number of likelihood samplers. It offers a fully featured Bayesian interface to galaxy model fitting (also called profiling), using mostly the same standard inputs as other popular codes (e.g. GALFIT ascl:1104.010), but it is also able to use complex priors and a number of likelihoods.
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.
Variational Bayesian Approximation methods for inverse problems
Mohammad-Djafari, Ali
2012-09-01
Variational Bayesian Approximation (VBA) methods are recent tools for effective Bayesian computations. In this paper, these tools are used for inverse problems where the prior models include hidden variables and where where the estimation of the hyper parameters has also to be addressed. In particular two specific prior models (Student-t and mixture of Gaussian models) are considered and details of the algorithms are given.
Bayesian Modeling of a Human MMORPG Player
Synnaeve, Gabriel
2010-01-01
This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.
Bayesian Modeling of a Human MMORPG Player
Synnaeve, Gabriel; Bessière, Pierre
2011-03-01
This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.
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.
Schönhofer, Axel L; McCormack, Maureen; Tsurusaki, Nobuo; Martens, Jochen; Hedin, Marshal
2013-01-01
We investigated the phylogeny and biogeographic history of the Holarctic harvestmen genus Sabacon, which shows an intercontinental disjunct distribution and is presumed to be a relatively old taxon. Molecular phylogenetic relationships of Sabacon were estimated using multiple gene regions and Bayesian inference for a comprehensive Sabacon sample. Molecular clock analyses, using relaxed clock models implemented in BEAST, are applied to date divergence events. Biogeographic scenarios utilizing S-DIVA and Lagrange C++ are reconstructed over sets of Bayesian trees, allowing for the incorporation of phylogenetic uncertainty and quantification of alternative reconstructions over time. Four primary well-supported subclades are recovered within Sabacon: (1) restricted to western North America; (2) eastern North American S. mitchelli and sampled Japanese taxa; (3) a second western North American group and taxa from Nepal and China; and (4) eastern North American S. cavicolens with sampled European Sabacon species. Three of four regional faunas (wNA, eNA, East Asia) are thereby non-monophyletic, and three clades include intercontinental disjuncts. Molecular clock analyses and biogeographic reconstructions support nearly simultaneous intercontinental dispersal coincident with the Eocene-Oligocene transition. We hypothesize that biogeographic exchange in the mid-Tertiary is likely correlated with the onset of global cooling, allowing cryophilic Sabacon taxa to disperse within and among continents. Morphological variation supports the divergent genetic clades observed in Sabacon, and suggests that a taxonomic revision (e.g., splitting Sabacon into multiple genera) may be warranted.
Understanding the formation and evolution of interstellar ices: a Bayesian approach
Energy Technology Data Exchange (ETDEWEB)
Makrymallis, Antonios; Viti, Serena, E-mail: antonios@star.ucl.ac.uk [Department of Physics and Astronomy, University College London, London WC1E 6BT (United Kingdom)
2014-10-10
Understanding the physical conditions of dark molecular clouds and star-forming regions is an inverse problem subject to complicated chemistry that varies nonlinearly with both time and the physical environment. In this paper, we apply a Bayesian approach based on a Markov chain Monte Carlo (MCMC) method for solving the nonlinear inverse problems encountered in astrochemical modeling. We use observations for ice and gas species in dark molecular clouds and a time-dependent, gas-grain chemical model to infer the values of the physical and chemical parameters that characterize quiescent regions of molecular clouds. We show evidence that in high-dimensional problems, MCMC algorithms provide a more efficient and complete solution than more classical strategies. The results of our MCMC method enable us to derive statistical estimates and uncertainties for the physical parameters of interest as a result of the Bayesian treatment.
Compaction and relaxation of biofilms
Valladares Linares, R.
2015-06-18
Operation of membrane systems for water treatment can be seriously hampered by biofouling. A better characterization of biofilms in membrane systems and their impact on membrane performance may help to develop effective biofouling control strategies. The objective of this study was to determine the occurrence, extent and timescale of biofilm compaction and relaxation (decompaction), caused by permeate flux variations. The impact of permeate flux changes on biofilm thickness, structure and stiffness was investigated in situ and non-destructively with optical coherence tomography using membrane fouling monitors operated at a constant crossflow velocity of 0.1 m s−1 with permeate production. The permeate flux was varied sequentially from 20 to 60 and back to 20 L m−2 h−1. The study showed that the average biofilm thickness on the membrane decreased after elevating the permeate flux from 20 to 60 L m−2 h−1 while the biofilm thickness increased again after restoring the original flux of 20 L m−2 h−1, indicating the occurrence of biofilm compaction and relaxation. Within a few seconds after the flux change, the biofilm thickness was changed and stabilized, biofilm compaction occurred faster than the relaxation after restoring the original permeate flux. The initial biofilm parameters were not fully reinstated: the biofilm thickness was reduced by 21%, biofilm stiffness had increased and the hydraulic biofilm resistance was elevated by 16%. Biofilm thickness was related to the hydraulic biofilm resistance. Membrane performance losses are related to the biofilm thickness, density and morphology, which are influenced by (variations in) hydraulic conditions. A (temporarily) permeate flux increase caused biofilm compaction, together with membrane performance losses. The impact of biofilms on membrane performance can be influenced (increased and reduced) by operational parameters. The article shows that a (temporary) pressure increase leads to more
First-principles study of fully relaxed vacancies in GaAs
Laasonen, K; Nieminen, Risto M.; Puska, Martti J.
1992-01-01
The structural and electronic properties of vacancies in GaAs have been studied using ab initio molecular dynamics. The atomic structures of vacancies in different charge states have been optimized by using a simulated-annealing procedure. The neighbor-atom relaxations are modest for neutral, singly negative, and doubly negative Ga vacancies as well as for the neutral As vacancy. In the case of singly and doubly negative As vacancies, very strong inward relaxations are found. These inward rel...
Spectral Estimation of NMR Relaxation
Naugler, David G.; Cushley, Robert J.
2000-08-01
In this paper, spectral estimation of NMR relaxation is constructed as an extension of Fourier Transform (FT) theory as it is practiced in NMR or MRI, where multidimensional FT theory is used. nD NMR strives to separate overlapping resonances, so the treatment given here deals primarily with monoexponential decay. In the domain of real error, it is shown how optimal estimation based on prior knowledge can be derived. Assuming small Gaussian error, the estimation variance and bias are derived. Minimum bias and minimum variance are shown to be contradictory experimental design objectives. The analytical continuation of spectral estimation is constructed in an optimal manner. An important property of spectral estimation is that it is phase invariant. Hence, hypercomplex data storage is unnecessary. It is shown that, under reasonable assumptions, spectral estimation is unbiased in the context of complex error and its variance is reduced because the modulus of the whole signal is used. Because of phase invariance, the labor of phasing and any error due to imperfect phase can be avoided. A comparison of spectral estimation with nonlinear least squares (NLS) estimation is made analytically and with numerical examples. Compared to conventional sampling for NLS estimation, spectral estimation would typically provide estimation values of comparable precision in one-quarter to one-tenth of the spectrometer time when S/N is high. When S/N is low, the time saved can be used for signal averaging at the sampled points to give better precision. NLS typically provides one estimate at a time, whereas spectral estimation is inherently parallel. The frequency dimensions of conventional nD FT NMR may be denoted D1, D2, etc. As an extension of nD FT NMR, one can view spectral estimation of NMR relaxation as an extension into the zeroth dimension. In nD NMR, the information content of a spectrum can be extracted as a set of n-tuples (ω1, … ωn), corresponding to the peak maxima
Philosophy and the practice of Bayesian statistics.
Gelman, Andrew; Shalizi, Cosma Rohilla
2013-02-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework.
Werbeck, Nicolas D.; Hansen, D. Flemming
2014-01-01
The equations that describe the time-evolution of transverse and longitudinal 15N magnetisations in tetrahedral ammonium ions, 15NH4+, are derived from the Bloch-Wangsness-Redfield density operator relaxation theory. It is assumed that the relaxation of the spin-states is dominated by (1) the intra-molecular 15N–1H and 1H–1H dipole–dipole interactions and (2) interactions of the ammonium protons with remote spins, which also include the contribution to the relaxations that arise from the exchange of the ammonium protons with the bulk solvent. The dipole–dipole cross-correlated relaxation mechanisms between each of the 15N–1H and 1H–1H interactions are explicitly taken into account in the derivations. An application to 15N-ammonium bound to a 41 kDa domain of the protein DnaK is presented, where a comparison between experiments and simulations show that the ammonium ion rotates rapidly within its binding site with a local correlation time shorter than approximately 1 ns. The theoretical framework provided here forms the basis for further investigations of dynamics of AX4 spin systems, with ammonium ions in solution and bound to proteins of particular interest. PMID:25128779
Werbeck, Nicolas D; Hansen, D Flemming
2014-09-01
The equations that describe the time-evolution of transverse and longitudinal (15)N magnetisations in tetrahedral ammonium ions, (15)NH4(+), are derived from the Bloch-Wangsness-Redfield density operator relaxation theory. It is assumed that the relaxation of the spin-states is dominated by (1) the intra-molecular (15)N-(1)H and (1)H-(1)H dipole-dipole interactions and (2) interactions of the ammonium protons with remote spins, which also include the contribution to the relaxations that arise from the exchange of the ammonium protons with the bulk solvent. The dipole-dipole cross-correlated relaxation mechanisms between each of the (15)N-(1)H and (1)H-(1)H interactions are explicitly taken into account in the derivations. An application to (15)N-ammonium bound to a 41kDa domain of the protein DnaK is presented, where a comparison between experiments and simulations show that the ammonium ion rotates rapidly within its binding site with a local correlation time shorter than approximately 1ns. The theoretical framework provided here forms the basis for further investigations of dynamics of AX4 spin systems, with ammonium ions in solution and bound to proteins of particular interest.
Structural relaxation of acridine orange dimer in bulk water and inside a single live lung cell
Chowdhury, Rajdeep; Nandi, Somen; Halder, Ritaban; Jana, Biman; Bhattacharyya, Kankan
2016-02-01
Structural relaxation of the acridine orange (AO) dimer in bulk water and inside a single live lung cell is studied using time resolved confocal microscopy and molecular dynamics (MD) simulations. The emission maxima ( λem max ˜ 630 nm) of AO in a lung cancer cell (A549) and a non-cancer lung fibroblast cell (WI38) suggest that AO exists as a dimer inside the cell. Time-dependent red shift in emission maximum indicates dynamic relaxation of the AO dimer (in the excited state) with a time constant of 500-600 ps, both in bulk water and inside the cell. We have calculated the equilibrium relaxation dynamics of the AO dimer in the ground state using MD simulations and found a slow component of time scale ˜350 ps. The intra- and inter-molecular components of the total relaxation dynamics of the AO dimer reveal the presence of a slow component of the order of a few hundred picoseconds. Upon restricting intra-molecular dye dynamics by harmonic constraint between AO monomers, the slow component vanishes. Combining the experimental observations and MD simulation results, we ascribe the slow component of the dynamic relaxation of the AO dimer to the structural relaxation, namely, fluctuations in the distance between the two monomers and associated fluctuation in the number of water molecules.
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.
Institute of Scientific and Technical Information of China (English)
Stefanie M. ICKERT-BOND; Catarina RYDIN; Susanne S. RENNER
2009-01-01
Ephedra comprises approximately 50 species, which are roughly equally distributed between the Old and New World deserts, but not in the intervening regions (amphitropical range). Great heterogeneity in the substitution rates of Gnetales (Ephedra, Gnetum, and Welwitschia) has made it difficult to infer the ages of the major divergence events in Ephedra, such as the timing of the Beringian disjunction in the genus and the entry into South America. Here, we use data from as many Gnetales species and genes as available from GenBank and from a recent study to investigate the timing of the major divergence events. Because of the tradeoff between the amount of missing data and taxon/gene sampling, we reduced the initial matrix of 265 accessions and 12 loci to 95 accessions and 10 loci, and further to 42 species (and 7736 aligned nucleotides) to achieve stationary distributions in the Bayesian molecular clock runs. Results from a relaxed clock with an uncorrelated rates model and fossil-based calibration reveal that New World species are monophyletic and diverged from their mostly Asian sister clade some 30 mya, fitting with many other Beringian disjunctions. The split between the single North American and the single South American clade occurred approximately 25 mya, well before the closure of the Panamanian Isthmus. Overall, the biogeographic history of Ephedra appears dominated by long-distance dispersal, but finer-scale studies are needed to test this hypothesis.
Glass transition and relaxation dynamics of propylene glycol-water solutions confined in clay
Elamin, Khalid; Björklund, Jimmy; Nyhlén, Fredrik; Yttergren, Madeleine; Mârtensson, Lena; Swenson, Jan
2014-07-01
The molecular dynamics of aqueous solutions of propylene glycol (PG) and propylene glycol methylether (PGME) confined in a two-dimensional layer-structured Na-vermiculite clay has been studied by broadband dielectric spectroscopy and differential scanning calorimetry. As typical for liquids in confined geometries the intensity of the cooperative α-relaxation becomes considerably more suppressed than the more local β-like relaxation processes. In fact, at high water contents the calorimetric glass transition and related structural α-relaxation cannot even be observed, due to the confinement. Thus, the intensity of the viscosity related α-relaxation is dramatically reduced, but its time scale as well as the related glass transition temperature Tg are for both systems only weakly influenced by the confinement. In the case of the PGME-water solutions it is an important finding since in the corresponding bulk system a pronounced non-monotonic concentration dependence of the glass transition related dynamics has been observed due to the growth of hydrogen bonded relaxing entities of water bridging between PGME molecules [J. Sjöström, J. Mattsson, R. Bergman, and J. Swenson, Phys. Chem. B 115, 10013 (2011)]. The present results suggest that the same type of structural entities are formed in the quasi-two-dimensional space between the clay platelets. It is also observed that the main water relaxation cannot be distinguished from the β-relaxation of PG or PGME in the concentration range up to intermediate water contents. This suggests that these two processes are coupled and that the water molecules affect the time scale of the β-relaxation. However, this is most likely true also for the corresponding bulk solutions, which exhibit similar time scales of this combined relaxation process below Tg. Finally, it is found that at higher water contents the water relaxation does not merge with, or follow, the α-relaxation above Tg, but instead crosses the α-relaxation
Bayesian inference for OPC modeling
Burbine, Andrew; Sturtevant, John; Fryer, David; Smith, Bruce W.
2016-03-01
The use of optical proximity correction (OPC) demands increasingly accurate models of the photolithographic process. Model building and inference techniques in the data science community have seen great strides in the past two decades which make better use of available information. This paper aims to demonstrate the predictive power of Bayesian inference as a method for parameter selection in lithographic models by quantifying the uncertainty associated with model inputs and wafer data. Specifically, the method combines the model builder's prior information about each modelling assumption with the maximization of each observation's likelihood as a Student's t-distributed random variable. Through the use of a Markov chain Monte Carlo (MCMC) algorithm, a model's parameter space is explored to find the most credible parameter values. During parameter exploration, the parameters' posterior distributions are generated by applying Bayes' rule, using a likelihood function and the a priori knowledge supplied. The MCMC algorithm used, an affine invariant ensemble sampler (AIES), is implemented by initializing many walkers which semiindependently explore the space. The convergence of these walkers to global maxima of the likelihood volume determine the parameter values' highest density intervals (HDI) to reveal champion models. We show that this method of parameter selection provides insights into the data that traditional methods do not and outline continued experiments to vet the method.
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 ...
Relaxation of liquid bridge after droplets coalescence
Directory of Open Access Journals (Sweden)
Jiangen Zheng
2016-11-01
Full Text Available We investigate the relaxation of liquid bridge after the coalescence of two sessile droplets resting on an organic glass substrate both experimentally and theoretically. The liquid bridge is found to relax to its equilibrium shape via two distinct approaches: damped oscillation relaxation and underdamped relaxation. When the viscosity is low, damped oscillation shows up, in this approach, the liquid bridge undergoes a damped oscillation process until it reaches its stable shape. However, if the viscous effects become significant, underdamped relaxation occurs. In this case, the liquid bridge relaxes to its equilibrium state in a non-periodic decay mode. In depth analysis indicates that the damping rate and oscillation period of damped oscillation are related to an inertial-capillary time scale τc. These experimental results are also testified by our numerical simulations with COMSOL Multiphysics.
Cross relaxation in nitroxide spin labels
Marsh, Derek
2016-11-01
Cross relaxation, and mI -dependence of the intrinsic electron spin-lattice relaxation rate We , are incorporated explicitly into the rate equations for the electron-spin population differences that govern the saturation behaviour of 14N- and 15N-nitroxide spin labels. Both prove important in spin-label EPR and ELDOR, particularly for saturation recovery studies. Neither for saturation recovery, nor for CW-saturation EPR and CW-ELDOR, can cross relaxation be described simply by increasing the value of We , the intrinsic spin-lattice relaxation rate. Independence of the saturation recovery rates from the hyperfine line pumped or observed follows directly from solution of the rate equations including cross relaxation, even when the intrinsic spin-lattice relaxation rate We is mI -dependent.
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.
Divergence date estimation and a comprehensive molecular tree of extant cetaceans.
McGowen, Michael R; Spaulding, Michelle; Gatesy, John
2009-12-01
Cetaceans are remarkable among mammals for their numerous adaptations to an entirely aquatic existence, yet many aspects of their phylogeny remain unresolved. Here we merged 37 new sequences from the nuclear genes RAG1 and PRM1 with most published molecular data for the group (45 nuclear loci, transposons, mitochondrial genomes), and generated a supermatrix consisting of 42,335 characters. The great majority of these data have never been combined. Model-based analyses of the supermatrix produced a solid, consistent phylogenetic hypothesis for 87 cetacean species. Bayesian analyses corroborated odontocete (toothed whale) monophyly, stabilized basal odontocete relationships, and completely resolved branching events within Mysticeti (baleen whales) as well as the problematic speciose clade Delphinidae (oceanic dolphins). Only limited conflicts relative to maximum likelihood results were recorded, and discrepancies found in parsimony trees were very weakly supported. We utilized the Bayesian supermatrix tree to estimate divergence dates among lineages using relaxed-clock methods. Divergence estimates revealed rapid branching of basal odontocete lineages near the Eocene-Oligocene boundary, the antiquity of river dolphin lineages, a Late Miocene radiation of balaenopteroid mysticetes, and a recent rapid radiation of Delphinidae beginning approximately 10 million years ago. Our comprehensive, time-calibrated tree provides a powerful evolutionary tool for broad-scale comparative studies of Cetacea.
Grain Alignment: Role of Radiative Torques and Paramagnetic Relaxation
Lazarian, A; Hoang, Thiem
2015-01-01
Polarization arising from aligned dust grains presents a unique opportunity to study magnetic fields in the diffuse interstellar medium and molecular clouds. Polarization from circumstellar regions, accretion disks and comet atmospheres can also be related to aligned dust.To reliably trace magnetic fields quantitative theory of grain alignment is required. Formulating the theory that would correspond to observations was one of the longstanding problems in astrophysics. Lately this problem has been successfully addressed and in this review we summarize some of the most important theoretical advances in the theory of grain alignment by radiative torques (RATs) that act on realistic irregular dust grains. We discuss an analytical model of RATs and the ways to make RAT alignment more efficient, e.g. through paramagnetic relaxation when grains have inclusions with strong magnetic response. For very small grains for which RAT alignment is inefficient, we also discuss paramagnetic relaxation and a process termed res...
Liquid-state paramagnetic relaxation from first principles
Rantaharju, Jyrki; Vaara, Juha
2016-10-01
We simulate nuclear and electron spin relaxation rates in a paramagnetic system from first principles. Sampling a molecular dynamics trajectory with quantum-chemical calculations produces a time series of the instantaneous parameters of the relevant spin Hamiltonian. The Hamiltonians are, in turn, used to numerically solve the Liouville-von Neumann equation for the time evolution of the spin density matrix. We demonstrate the approach by studying the aqueous solution of the Ni2 + ion. Taking advantage of Kubo's theory, the spin-lattice (T1) and spin-spin (T2) relaxation rates are extracted from the simulations of the time dependence of the longitudinal and transverse magnetization, respectively. Good agreement with the available experimental data is obtained by the method.
Relativistic frozen core potential scheme with relaxation of core electrons
Nakajima, Yuya; Seino, Junji; Hayami, Masao; Nakai, Hiromi
2016-10-01
This letter proposes a relaxation scheme for core electrons based on the frozen core potential method at the infinite-order Douglas-Kroll-Hess level, called FCP-CR. The core electrons are self-consistently relaxed using frozen molecular valence potentials after the valence SCF calculation is performed. The efficiency of FCP-CR is confirmed by calculations of gold clusters. Furthermore, FCP-CR reproduces the results of the all-electron method for the energies of coinage metal dimers and the core ionization energies and core level shifts of vinyl acetate and three tungsten complexes at the Hartree-Fock and/or symmetry-adapted cluster configuration interaction levels.
Utilizing RELAX NG Schemas in XML Editors
Schmied, Martin
2008-01-01
This thesis explores the possibilities of utilizing RELAX NG schemata in the process of editing XML documents. The ultimate goal of this thesis is to prototype a system supporting user while editing XML document with bound RELAX NG schema inside the Eclipse IDE. Such a system comprises two major components -- an integration of RELAX NG validator and an autocompletion engine. Design of the autocompletion engine represents the main contribution of this thesis, because similar systems are almost...
Temperature relaxation in dense plasma mixtures
Faussurier, Gérald; Blancard, Christophe
2016-09-01
We present a model to calculate temperature-relaxation rates in dense plasma mixtures. The electron-ion relaxation rates are calculated using an average-atom model and the ion-ion relaxation rates by the Landau-Spitzer approach. This method allows the study of the temperature relaxation in many-temperature electron-ion and ion-ion systems such as those encountered in inertial confinement fusion simulations. It is of interest for general nonequilibrium thermodynamics dealing with energy flows between various systems and should find broad use in present high energy density experiments.
Baryogenesis via Elementary Goldstone Higgs Relaxation
Gertov, Helene; Pearce, Lauren; Yang, Louis
2016-01-01
We extend the relaxation mechanism to the Elementary Goldstone Higgs frame- work. Besides studying the allowed parameter space of the theory we add the minimal ingredients needed for the framework to be phenomenologically viable. The very nature of the extended Higgs sector allows to consider very flat scalar potential directions along which the relaxation mechanism can be implemented. This fact translates into wider regions of applicability of the relaxation mechanism when compared to the Standard Model Higgs case. Our results show that, if the electroweak scale is not fundamental but radiatively generated, it is possible to generate the observed matter-antimatter asymmetry via the relaxation mechanism.
Relaxation and Visualization Strategies for Story Telling
Institute of Scientific and Technical Information of China (English)
冯灵林
2012-01-01
The importance of training students to tell or retell story is self - evident for mastering English language. The following activity introduces relaxation and visualization strategies for story telling.
Dielectric relaxation studies in polyvinyl butyral
Mehendru, P. C.; Kumar, Naresh; Arora, V. P.; Gupta, N. P.
1982-10-01
Dielectric measurements have been made in thick films (˜100 μm) of polyvinyl butyral (PVB) having degree of polymerization n=1600, in the frequency range 100 Hz-100 KHz and temperature range 300-373 K. The results indicated that PVB was in the amorphous phase and observed dielectric dispersion has been assigned as the β-relaxation process. The β relaxation is of Debye type with symmetrical distribution of relaxation times. The dielectric relaxation strength Δɛ and the distribution parameters β¯ increase with temperature. The results can be qualitatively explained by assuming the hindered rotation of the side groups involving hydroxyl/acetate groups.
Mechanisms of relaxation and spin decoherence in nanomagnets
van Tol, Johan
Relaxation in spin systems is of great interest with respect to various possible applications like quantum information processing and storage, spintronics, and dynamic nuclear polarization (DNP). The implementation of high frequencies and fields is crucial in the study of systems with large zero-field splitting or large interactions, as for example molecular magnets and low dimensional magnetic materials. Here we will focus on the implementation of pulsed Electron Paramagnetic Resonance (ERP) at multiple frequencies of 10, 95, 120, 240, and 336 GHz, and the relaxation and decoherence processes as a function of magnetic field and temperature. Firstly, at higher frequencies the direct single-phonon spin-lattice relaxation (SLR) is considerably enhanced, and will more often than not be the dominant relaxation mechanism at low temperatures, and can be much faster than at lower fields and frequencies. In principle the measurement of the SLR rates as a function of the frequency provides a means to map the phonon density of states. Secondly, the high electron spin polarization at high fields has a strong influence on the spin fluctuations in relatively concentrated spin systems, and the contribution of the electron-electron dipolar interactions to the coherence rate can be partially quenched at low temperatures. This not only allows the study of relatively concentrated spin systems by pulsed EPR (as for example magnetic nanoparticles and molecular magnets), it enables the separation of the contribution of the fluctuations of the electron spin system from other decoherence mechanisms. Besides choice of temperature and field, several strategies in sample design, pulse sequences, or clock transitions can be employed to extend the coherence time in nanomagnets. A review will be given of the decoherence mechanisms with an attempt at a quantitative comparison of experimental rates with theory.
Relaxing Chosen-Ciphertext Security
DEFF Research Database (Denmark)
Canetti, Ran; Krawczyk, Hugo; Nielsen, Jesper Buus
2003-01-01
Security against adaptive chosen ciphertext attacks (or, CCA security) has been accepted as the standard requirement from encryption schemes that need to withstand active attacks. In particular, it is regarded as the appropriate security notion for encryption schemes used as components within...... general protocols and applications. Indeed, CCA security was shown to suffice in a large variety of contexts. However, CCA security often appears to be somewhat too strong: there exist encryption schemes (some of which come up naturally in practice) that are not CCA secure, but seem sufficiently secure...... “for most practical purposes.” We propose a relaxed variant of CCA security, called Replayable CCA (RCCA) security. RCCA security accepts as secure the non-CCA (yet arguably secure) schemes mentioned above; furthermore, it suffices for most existing applications of CCA security. We provide three...
Wang, Meng; Li, Xiangqian; Guo, Yuxing; Wu, Tao; Liu, Ying Dan; Ngai, K. L.; Wang, Li-Min
2016-12-01
Found in our recent dielectric study of a planar and rigid glass-former, 1-methylindole (1MID), is an unusual secondary relaxation unrelated in its dynamic properties to the structural α-relaxation. We speculated that it originates from the in-plane motion of the molecules, and the supposedly universal Johari-Goldstein (JG) β-relaxation with strong connection to the structural α-relaxation in rigid glass-formers is not resolved [X. Q. Li et al. J. Chem. Phys. 143, 104505 (2015)]. In this work, dielectric measurements are performed in binary mixtures of 1MID with two aromatics of weak polarity, ethylbenzene (EB) and triphenylethylene (TPE), in the highly viscous regimes near glass transition. EB and TPE have smaller and larger molecular sizes and glass transition temperatures Tg than 1MID, respectively. Strikingly, the results show that the resolved secondary relaxations of 1MID in the two mixtures share the same relaxation time and their temperature dependence as pure 1MID, independent of the mode and degree of dilution. The results indicate that the unusual secondary relaxation is not directly coupled with the α-relaxation, and support the in-plane-rotation interpretation of its origin. On the other hand, the supposedly universal and intermolecular JG β-relaxation coming from the out-of-plane motion of the planar molecule has weaker dielectric strength, and it cannot be resolved from the more intense in-plane-rotation secondary relaxation because the dipole moment of 1MID lies on the plane.
Bayesian Calibration of Microsimulation Models.
Rutter, Carolyn M; Miglioretti, Diana L; Savarino, James E
2009-12-01
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models.
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.
Bayesian modeling of flexible cognitive control.
Jiang, Jiefeng; Heller, Katherine; Egner, Tobias
2014-10-01
"Cognitive control" describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation.
Multi-Fraction Bayesian Sediment Transport Model
Directory of Open Access Journals (Sweden)
Mark L. Schmelter
2015-09-01
Full Text Available A Bayesian approach to sediment transport modeling can provide a strong basis for evaluating and propagating model uncertainty, which can be useful in transport applications. Previous work in developing and applying Bayesian sediment transport models used a single grain size fraction or characterized the transport of mixed-size sediment with a single characteristic grain size. Although this approach is common in sediment transport modeling, it precludes the possibility of capturing processes that cause mixed-size sediments to sort and, thereby, alter the grain size available for transport and the transport rates themselves. This paper extends development of a Bayesian transport model from one to k fractional dimensions. The model uses an existing transport function as its deterministic core and is applied to the dataset used to originally develop the function. The Bayesian multi-fraction model is able to infer the posterior distributions for essential model parameters and replicates predictive distributions of both bulk and fractional transport. Further, the inferred posterior distributions are used to evaluate parametric and other sources of variability in relations representing mixed-size interactions in the original model. Successful OPEN ACCESS J. Mar. Sci. Eng. 2015, 3 1067 development of the model demonstrates that Bayesian methods can be used to provide a robust and rigorous basis for quantifying uncertainty in mixed-size sediment transport. Such a method has heretofore been unavailable and allows for the propagation of uncertainty in sediment transport applications.
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.
Bayesian modeling of flexible cognitive control
Jiang, Jiefeng; Heller, Katherine; Egner, Tobias
2014-01-01
“Cognitive control” describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. PMID:24929218
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.
MAP estimators and their consistency in Bayesian nonparametric inverse problems
Dashti, M.; Law, K. J. H.; Stuart, A. M.; Voss, J.
2013-09-01
We consider the inverse problem of estimating an unknown function u from noisy measurements y of a known, possibly nonlinear, map {G} applied to u. We adopt a Bayesian approach to the problem and work in a setting where the prior measure is specified as a Gaussian random field μ0. We work under a natural set of conditions on the likelihood which implies the existence of a well-posed posterior measure, μy. Under these conditions, we show that the maximum a posteriori (MAP) estimator is well defined as the minimizer of an Onsager-Machlup functional defined on the Cameron-Martin space of the prior; thus, we link a problem in probability with a problem in the calculus of variations. We then consider the case where the observational noise vanishes and establish a form of Bayesian posterior consistency for the MAP estimator. We also prove a similar result for the case where the observation of {G}(u) can be repeated as many times as desired with independent identically distributed noise. The theory is illustrated with examples from an inverse problem for the Navier-Stokes equation, motivated by problems arising in weather forecasting, and from the theory of conditioned diffusions, motivated by problems arising in molecular dynamics.
MAP estimators and their consistency in Bayesian nonparametric inverse problems
Dashti, M.
2013-09-01
We consider the inverse problem of estimating an unknown function u from noisy measurements y of a known, possibly nonlinear, map applied to u. We adopt a Bayesian approach to the problem and work in a setting where the prior measure is specified as a Gaussian random field μ0. We work under a natural set of conditions on the likelihood which implies the existence of a well-posed posterior measure, μy. Under these conditions, we show that the maximum a posteriori (MAP) estimator is well defined as the minimizer of an Onsager-Machlup functional defined on the Cameron-Martin space of the prior; thus, we link a problem in probability with a problem in the calculus of variations. We then consider the case where the observational noise vanishes and establish a form of Bayesian posterior consistency for the MAP estimator. We also prove a similar result for the case where the observation of can be repeated as many times as desired with independent identically distributed noise. The theory is illustrated with examples from an inverse problem for the Navier-Stokes equation, motivated by problems arising in weather forecasting, and from the theory of conditioned diffusions, motivated by problems arising in molecular dynamics. © 2013 IOP Publishing Ltd.
Energy Dissipation in Molecular Systems
Tramer, André; Lahmani, Fran oise
2005-01-01
Energy Dissipation in Molecular Systems analyzes experimental data on the redistribution and dissipation of energy injected into molecular systems by radiation or charged particles. These processes, competing with such practically important relaxation channels as chemical reaction or stimulated emission (laser action), are the primary focus in this monograph. Among other topics, the book treats vibrational redistribution and electronic relaxation in isolated molecules and the effects of inter-molecular interactions (collisions, complex formation, solvent effects) on the relaxation paths. Primary photo-chemical processes (such as isomerization, proton or hydrogen-atom transfer, electron transfer and ionization) are also treated as particular cases of vibrational or electronic relaxation. Only a basic knowledge of quantum mechanics and spectroscopy is assumed and calculations are kept to a strict minimum, making the book more accessible to students.
Improving standard practices for prediction in ungauged basins: Bayesian approach
Prieto, Cristina; Le-Vine, Nataliya; García, Eduardo; Medina, Raúl
2015-04-01
In hydrological modelling, the prediction of flows in ungauged basins is still a defiance. Among the different alternatives to quantify and reduce the uncertainty in the predictions, a Bayesian framework has proven to be advantageous. This framework allows flow prediction in ungauged basins based on regionalised hydrological indices. Being grounded on probability theory, the procedure requires a number of assumptions and decisions to be made. Among the most important ones are 1) selection of representative hydrological signatures, 2) selection of regionalization model functional form, and 3) a 'perfect' model/ input assumption. The contribution of this research is to address these three assumptions. First, to reduce an extensive set of available hydrological signatures we select a compact orthogonal set of information pieces using Principal Component Analysis. This advances the standard practice of semi-empirical selection of individual hydrological signatures. Second, we use functional-form-assumption-free Random Forests to regionalize the selected information. This allows the traditional assumption of linear regression between catchment properties and characteristics of hydrological response to be relaxes. And third, we propose utilizing non-traditional metrics to flag-up possible model/ input errors: Bayes' Factor and a newly-proposed 'Suitability' test. This addresses the typical non-realistic assumption that model is 'perfect' and the input is noise-free. The proposed methodological developments are illustrated for the empirical challenge of flow prediction in rivers in Northern Spain.
Bayesian Inference in Polling Technique: 1992 Presidential Polls.
Satake, Eiki
1994-01-01
Explores the potential utility of Bayesian statistical methods in determining the predictability of multiple polls. Compares Bayesian techniques to the classical statistical method employed by pollsters. Considers these questions in the context of the 1992 presidential elections. (HB)
Bayesian modeling of unknown diseases for biosurveillance.
Shen, Yanna; Cooper, Gregory F
2009-11-14
This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this paper is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in medical informatics, where the space of known causes of outcomes of interest is seldom complete.
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.
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...
Event generator tuning using Bayesian optimization
Ilten, Philip; Yang, Yunjie
2016-01-01
Monte Carlo event generators contain a large number of parameters that must be determined by comparing the output of the generator with experimental data. Generating enough events with a fixed set of parameter values to enable making such a comparison is extremely CPU intensive, which prohibits performing a simple brute-force grid-based tuning of the parameters. Bayesian optimization is a powerful method designed for such black-box tuning applications. In this article, we show that Monte Carlo event generator parameters can be accurately obtained using Bayesian optimization and minimal expert-level physics knowledge. A tune of the PYTHIA 8 event generator using $e^+e^-$ events, where 20 parameters are optimized, can be run on a modern laptop in just two days. Combining the Bayesian optimization approach with expert knowledge should enable producing better tunes in the future, by making it faster and easier to study discrepancies between Monte Carlo and experimental data.
Learning Bayesian Networks from Correlated Data.
Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H; Perls, Thomas T; Sebastiani, Paola
2016-05-05
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.
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...
Bayesian 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...
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
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...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge 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 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.
Variational Bayesian Inference of Line Spectra
DEFF Research Database (Denmark)
Badiu, Mihai Alin; Hansen, Thomas Lundgaard; Fleury, Bernard Henri
2016-01-01
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coeffici......In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid......; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a more complete Bayesian treatment by estimating the posterior probability density functions (pdfs...
Hessian PDF reweighting meets the Bayesian methods
Paukkunen, Hannu
2014-01-01
We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\\chi^2$-fit and it naturally incorporates also non-zero values for the tolerance, $\\Delta\\chi^2>1$. In comparison to the contemporary Bayesian reweighting techniques, there is no need to generate large ensembles of PDF Monte-Carlo replicas, and the observables need to be evaluated only with the central and the error sets of the original PDFs. In spite of the apparently rather different methodologies, we find that the Hessian and the Bayesian techniques are actually equivalent if the $\\Delta\\chi^2$ criterion is properly included to the Bayesian likelihood function that is a simple exponential.
Bayesian analysis of MEG visual evoked responses
Energy Technology Data Exchange (ETDEWEB)
Schmidt, D.M.; George, J.S.; Wood, C.C.
1999-04-01
The authors developed a method for analyzing neural electromagnetic data that allows probabilistic inferences to be drawn about regions of activation. The method involves the generation of a large number of possible solutions which both fir the data and prior expectations about the nature of probable solutions made explicit by a Bayesian formalism. In addition, they have introduced a model for the current distributions that produce MEG and (EEG) data that allows extended regions of activity, and can easily incorporate prior information such as anatomical constraints from MRI. To evaluate the feasibility and utility of the Bayesian approach with actual data, they analyzed MEG data from a visual evoked response experiment. They compared Bayesian analyses of MEG responses to visual stimuli in the left and right visual fields, in order to examine the sensitivity of the method to detect known features of human visual cortex organization. They also examined the changing pattern of cortical activation as a function of time.
Bayesian Analysis of Perceived Eye Level
Orendorff, Elaine E.; Kalesinskas, Laurynas; Palumbo, Robert T.; Albert, Mark V.
2016-01-01
To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)—a self-reported measure of elevation angle. This framework provides a single, coherent model explaining a set of experimentally observed PEL over a range of experimental conditions. Further, it provides a parsimonious explanation for the additive effect of low fidelity cues as well as the averaging effect of high fidelity cues, as also found in other Bayesian cue combination psychophysical studies. Our model accurately estimates the PEL and explains the form of previous equations used in describing PEL behavior. Most importantly, the proposed Bayesian framework for PEL is more powerful than previous behavioral modeling; it permits behavioral estimation in a wider range of cue combination and perceptual studies than models previously reported. PMID:28018204
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 ...
Superparamagnetic relaxation of weakly interacting particles
DEFF Research Database (Denmark)
Mørup, Steen; Tronc, Elisabeth
1994-01-01
The influence of particle interactions on the superparamagnetic relaxation time has been studied by Mossbauer spectroscopy in samples of maghemite (gamma-Fe2O3) particles with different particle sizes and particle separations. It is found that the relaxation time decreases with decreasing particl...
Postextrasystolic relaxation in the dog heart
Kuijer, P.J.P.; Heethaar, R.M.; Herbschleb, J.N.; Zimmerman, A.N.E.; Meijler, F.L.
1978-01-01
Left ventricular relaxation was studied in 8 dogs using parameters derived from the left ventricular pressure: the fastest pressure fall and the time constant of pressure decline. Effects of extrasystolic rhythm interventions were examined on the relaxation parameters of the post-relative to the pre
Windowing Waveform Relaxation of Initial Value Problems
Institute of Scientific and Technical Information of China (English)
Yao-lin Jiang
2006-01-01
We present a windowing technique of waveform relaxation for dynamic systems. An effective estimation on window length is derived by an iterative error expression provided here. Relaxation processes can be speeded up if one takes the windowing technique in advance. Numerical experiments are given to further illustrate the theoretical analysis.
Superparamagnetic relaxation in alpha-Fe particles
DEFF Research Database (Denmark)
Bødker, Franz; Mørup, Steen; Pedersen, Michael Stanley;
1998-01-01
The superparamagnetic relaxation time of carbon-supported alpha-Fe particles with an average size of 3.0 Mm has been studied over a large temperature range by the use of Mossbauer spectroscopy combined with AC and DC magnetization measurements. It is found that the relaxation time varies with tem...
Cross relaxation in nitroxide spin labels
DEFF Research Database (Denmark)
Marsh, Derek
2016-01-01
-label EPR and ELDOR, particularly for saturation recovery studies. Neither for saturation recovery, nor for CW-saturation EPR and CW-ELDOR, can cross relaxation be described simply by increasing the value of We, the intrinsic spin-lattice relaxation rate. Independence of the saturation recovery rates from...
Elasticity, structure, and relaxation of extended proteins under force
Stirnemann, Guillaume; Giganti, David; Fernandez, Julio M.; Berne, B. J.
2013-01-01
Force spectroscopies have emerged as a powerful and unprecedented tool to study and manipulate biomolecules directly at a molecular level. Usually, protein and DNA behavior under force is described within the framework of the worm-like chain (WLC) model for polymer elasticity. Although it has been surprisingly successful for the interpretation of experimental data, especially at high forces, the WLC model lacks structural and dynamical molecular details associated with protein relaxation under force that are key to the understanding of how force affects protein flexibility and reactivity. We use molecular dynamics simulations of ubiquitin to provide a deeper understanding of protein relaxation under force. We find that the WLC model successfully describes the simulations of ubiquitin, especially at higher forces, and we show how protein flexibility and persistence length, probed in the force regime of the experiments, are related to how specific classes of backbone dihedral angles respond to applied force. Although the WLC model is an average, backbone model, we show how the protein side chains affect the persistence length. Finally, we find that the diffusion coefficient of the protein’s end-to-end distance is on the order of 108 nm2/s, is position and side-chain dependent, but is independent of the length and independent of the applied force, in contrast with other descriptions. PMID:23407163
Theory of Activated Relaxation in Nanoscale Confined Liquids
Mirigian, Stephen; Schweizer, Kenneth
2014-03-01
We extend the recently developed Elastically Cooperative Nonlinear Langevin Equation(ECNLE) theory of activated relaxation in supercooled liquids to treat the case of geometrically confined liquids. Generically, confinement of supercooled liquids leads to a speeding up of the dynamics(with a consequent depression of the glass transition temperature) extending on the order of tens of molecular diameters away from a free surface. At present, this behavior is not theoretically well understood. Our theory interprets the speed up in dynamics in terms of two coupled effects. First, a direct surface effect, extending two to three molecular diameters from a free surface, and related to a local rearrangement of molecules with a single cage. The second is a longer ranged ``confinement'' effect, extending tens of molecular diameters from a free surface and related to the long range elastic penalty necessary for a local rearrangement. The theory allows for the calculation of relaxation time and Tg profiles within a given geometry and first principles calculations of relevant length scales. Comparison to both dynamic and pseudo-thermodynamic measurements shows reasonable agreement to experiment with no adjustable parameters.
A Bayesian Analysis of Spectral ARMA Model
Directory of Open Access Journals (Sweden)
Manoel I. Silvestre Bezerra
2012-01-01
Full Text Available Bezerra et al. (2008 proposed a new method, based on Yule-Walker equations, to estimate the ARMA spectral model. In this paper, a Bayesian approach is developed for this model by using the noninformative prior proposed by Jeffreys (1967. The Bayesian computations, simulation via Markov Monte Carlo (MCMC is carried out and characteristics of marginal posterior distributions such as Bayes estimator and confidence interval for the parameters of the ARMA model are derived. Both methods are also compared with the traditional least squares and maximum likelihood approaches and a numerical illustration with two examples of the ARMA model is presented to evaluate the performance of the procedures.
Length Scales in Bayesian Automatic Adaptive Quadrature
Directory of Open Access Journals (Sweden)
Adam Gh.
2016-01-01
Full Text Available Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1–16 (2012] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule, mesoscopic (Simpson rule, and macroscopic (quadrature sums of high algebraic degrees of precision. Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.
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 long branch attraction bias and corrections.
Susko, Edward
2015-03-01
Previous work on the star-tree paradox has shown that Bayesian methods suffer from a long branch attraction bias. That work is extended to settings involving more taxa and partially resolved trees. The long branch attraction bias is confirmed to arise more broadly and an additional source of bias is found. A by-product of the analysis is methods that correct for biases toward particular topologies. The corrections can be easily calculated using existing Bayesian software. Posterior support for a set of two or more trees can thus be supplemented with corrected versions to cross-check or replace results. Simulations show the corrections to be highly effective.
From retrodiction to Bayesian quantum imaging
Speirits, Fiona C.; Sonnleitner, Matthias; Barnett, Stephen M.
2017-04-01
We employ quantum retrodiction to develop a robust Bayesian algorithm for reconstructing the intensity values of an image from sparse photocount data, while also accounting for detector noise in the form of dark counts. This method yields not only a reconstructed image but also provides the full probability distribution function for the intensity at each pixel. We use simulated as well as real data to illustrate both the applications of the algorithm and the analysis options that are only available when the full probability distribution functions are known. These include calculating Bayesian credible regions for each pixel intensity, allowing an objective assessment of the reliability of the reconstructed image intensity values.
A Bayesian Concept Learning Approach to Crowdsourcing
DEFF Research Database (Denmark)
Viappiani, Paolo Renato; Zilles, Sandra; Hamilton, Howard J.;
2011-01-01
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......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...... that our Bayesian strategies are effective even in large concept spaces with many uninformative experts....
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.
Stress and Relaxation in Relation to Personality
Directory of Open Access Journals (Sweden)
Harish Kumar Sharma
2011-09-01
Full Text Available Relaxation plays a significant role in facing stress. The aim of the present study is to see whether personality patterns determine an individual’s ability to relax. As a reaction to stress, coping is the best way to handle stress, which requires rational and conscious thinking. Does this ability to relax anyway facilitate coping reactions? A study was conducted on 100 college students. Results revealed that extraverts relax easily than introverts. In addition, if intelligence level is average or above average, relaxation does play a role in facilitating coping reactions. It suggests that in designing techniques of stress management, the personality and intelligence level must be taken into consideration to make techniques effective.
Xiong, Q. L.; Tian, X. G.; Lu, T. J.
2012-07-01
The thermoelastic response of thin gold films induced by femtosecond laser irradiation is numerically simulated using a modified combined two-temperature model (TTM) and molecular dynamics (MD) method, with focus placed upon the influence of the electron relaxation effect. The validity of the numerical approach is checked against existing experimental results. While the electron relaxation effect is found negligible when the laser duration is much longer than the electron thermal relaxation time, it becomes significant if the laser duration matches the electron relaxation time, especially when the former is much shorter than the latter. The characteristics of thermo-mechanical interaction in the thin film are analyzed, and the influence of temperature-dependent material properties upon the thermoelastic response of the film quantified.
Effects of Periodic Temperature Changes on Stress Relaxation of Chemically Treated Wood
Institute of Scientific and Technical Information of China (English)
Xie Manhua; Zhao Guangjie
2004-01-01
In order to clarify the relationship between the microstructural changes and the rheological behaviors of four chemically treated woods (delignified wood, hemicellulose-removed wood, DMSO swollen and decrystallization treated wood), the stress relaxation of wood with three different moisture contents was determined during periodic temperature changes. The experimental results show that after wood relaxation for 4 h at 25 °C, the stress decays sharply when the temperature increases and 2 h later the stress recovers again when the temperature drops back to the original point. The additional stress relaxation, produced after temperature begins to increase, is mainly caused by the thermal swelling, molecular thermal movement and the break of a part of residual hydrogen bonds. The number of hydrogen bonds and the size and amount of cavities in various treated woods greatly affect the magnitude of the additional relaxed stress and the recovery stress.
2006-01-01
Tuberculosis can be studied at the population level by genotyping strains of Mycobacterium tuberculosis isolated from patients. We use an approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen. This method is applied to a published data set from San Francisco of tuberculosis genotypes based on the marker ...
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 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...
Prior approval: the growth of Bayesian methods in psychology.
Andrews, Mark; Baguley, Thom
2013-02-01
Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.
A default Bayesian hypothesis test for ANOVA designs
Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.
2012-01-01
This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA desig
From arguments to constraints on a Bayesian network
Bex, F.J.; Renooij, S.
2016-01-01
In this paper, we propose a way to derive constraints for a Bayesian Network from structured arguments. Argumentation and Bayesian networks can both be considered decision support techniques, but are typically used by experts with different backgrounds. Bayesian network experts have the mathematical
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…
Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip.
Paesani, S; Gentile, A A; Santagati, R; Wang, J; Wiebe, N; Tew, D P; O'Brien, J L; Thompson, M G
2017-03-10
Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, nonfault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a silicon quantum photonic device. The approach is verified to be well suited for prethreshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed.
Bayesian reduced-order models for multiscale dynamical systems
Koutsourelakis, P S
2010-01-01
While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and their applicability over macroscopic time scales of physical interest is computationally infeasible or impractical. In complex systems, with limited physical insight on the coherent behavior of their constituents, the only available information is data obtained from simulations of the trajectories of huge numbers of degrees of freedom over microscopic time scales. This paper discusses a Bayesian approach to deriving probabilistic coarse-grained models that simultaneously address the problems of identifying appropriate reduced coordinates and the effective dynamics in this lower-dimensional representation. At the core of the models proposed lie simple, low-dimensional dynamical systems which serve as the building blocks of the global model. These approximate the latent, generating sources and parameterize the reduced-order dynamics. We d...
Vibrational relaxation and energy transfer of matrix isolated HCl and DCl
Energy Technology Data Exchange (ETDEWEB)
Wiesenfeld, J.M.
1977-12-01
Vibrational kinetic and spectroscopic studies have been performed on matrix-isolated HCl and DCl between 9 and 20 K. Vibrational relaxation rates for v = 2 and v = 1 were measured by a tunable infrared laser-induced, time-resolved fluorescence technique. In an Ar matrix, vibrational decay times are faster than radiative and it is found that HCl relaxes about 35 times more rapidly than CCl, in spite of the fact that HCl must transfer more energy to the lattice than DCl. This result is explained by postulating that the rate-determining step for vibrational relaxation produces a highly rotationally excited guest in a V yield R step; rotational relaxation into lattice phonons follows rapidly. HCl v = 1, but not v = 2, excitation rapidly diffuses through the sample by a resonant dipole-dipole vibrational energy transfer process. Molecular complexes, and in particular the HCl dimer, relax too rapidly for direct observation, less than or approximately 1 ..mu..s, and act as energy sinks in the energy diffusion process. The temperature dependence for all these processes is weak--less than a factor of two between 9 and 20 K. Vibrational relaxation of HCl in N/sub 2/ and O/sub 2/ matrices is unobservable, presumably due to rapid V yield V transfer to the host. A V yield R binary collision model for relaxation in solids is successful in explaining the HCl(DCl)/Ar results as well as results of other experimenters. The model considers relaxation to be the result of ''collisions'' due to molecular motion in quantized lattice normal modes--gas phase potential parameters can fit the matrix kinetic data.
Brownian relaxation of an inelastic sphere in air
Bird, G. A.
2016-06-01
The procedures that are used to calculate the forces and moments on an aerodynamic body in the rarefied gas of the upper atmosphere are applied to a small sphere of the size of an aerosol particle at sea level. While the gas-surface interaction model that provides accurate results for macroscopic bodies may not be appropriate for bodies that are comprised of only about a thousand atoms, it provides a limiting case that is more realistic than the elastic model. The paper concentrates on the transfer of energy from the air to an initially stationary sphere as it acquires Brownian motion. Individual particle trajectories vary wildly, but a clear relaxation process emerges from an ensemble average over tens of thousands of trajectories. The translational and rotational energies in equilibrium Brownian motion are determined. Empirical relationships are obtained for the mean translational and rotational relaxation times, the mean initial power input to the particle, the mean rates of energy transfer between the particle and air, and the diffusivity. These relationships are functions of the ratio of the particle mass to an average air molecule mass and the Knudsen number, which is the ratio of the mean free path in the air to the particle diameter. The ratio of the molecular radius to the particle radius also enters as a correction factor. The implications of Brownian relaxation for the second law of thermodynamics are discussed.
Relaxation and physical aging in network glasses: a review
Micoulaut, Matthieu
2016-06-01
Recent progress in the description of glassy relaxation and aging are reviewed for the wide class of network-forming materials such as GeO2, Ge x Se1-x , silicates (SiO2-Na2O) or borates (B2O3-Li2O), all of which have an important usefulness in domestic, geological or optoelectronic applications. A brief introduction of the glass transition phenomenology is given, together with the salient features that are revealed both from theory and experiments. Standard experimental methods used for the characterization of the slowing down of the dynamics are reviewed. We then discuss the important role played by aspects of network topology and rigidity for the understanding of the relaxation of the glass transition, while also permitting analytical predictions of glass properties from simple and insightful models based on the network structure. We also emphasize the great utility of computer simulations which probe the dynamics at the molecular level, and permit the calculation of various structure-related functions in connection with glassy relaxation and the physics of aging which reveal the non-equilibrium nature of glasses. We discuss the notion of spatial variations of structure which leads to the concept of ‘dynamic heterogeneities’, and recent results in relation to this important topic for network glasses are also reviewed.
Supervised Discrete Hashing With Relaxation.
Gui, Jie; Liu, Tongliang; Sun, Zhenan; Tao, Dacheng; Tan, Tieniu
2016-12-29
Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called ''supervised discrete hashing with relaxation'' (SDHR) based on ''supervised discrete hashing'' (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.
Individual organisms as units of analysis: Bayesian-clustering alternatives in population genetics.
Mank, Judith E; Avise, John C
2004-12-01
Population genetic analyses traditionally focus on the frequencies of alleles or genotypes in 'populations' that are delimited a priori. However, there are potential drawbacks of amalgamating genetic data into such composite attributes of assemblages of specimens: genetic information on individual specimens is lost or submerged as an inherent part of the analysis. A potential also exists for circular reasoning when a population's initial identification and subsequent genetic characterization are coupled. In principle, these problems are circumvented by some newer methods of population identification and individual assignment based on statistical clustering of specimen genotypes. Here we evaluate a recent method in this genre--Bayesian clustering--using four genotypic data sets involving different types of molecular markers in non-model organisms from nature. As expected, measures of population genetic structure (F(ST) and phiST) tended to be significantly greater in Bayesian a posteriori data treatments than in analyses where populations were delimited a priori. In the four biological contexts examined, which involved both geographic population structures and hybrid zones, Bayesian clustering was able to recover differentiated populations, and Bayesian assignments were able to identify likely population sources of specific individuals.
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
Plasma Relaxation Dynamics Moderated by Current Sheets
Dewar, Robert; Bhattacharjee, Amitava; Yoshida, Zensho
2014-10-01
Ideal magnetohydrodynamics (IMHD) is strongly constrained by an infinite number of microscopic constraints expressing mass, entropy and magnetic flux conservation in each infinitesimal fluid element, the latter preventing magnetic reconnection. By contrast, in the Taylor-relaxed equilibrium model all these constraints are relaxed save for global magnetic flux and helicity. A Lagrangian is presented that leads to a new variational formulation of magnetized fluid dynamics, relaxed MHD (RxMHD), all static solutions of which are Taylor equilibrium states. By postulating that some long-lived macroscopic current sheets can act as barriers to relaxation, separating the plasma into multiple relaxation regions, a further generalization, multi-relaxed MHD (MRxMHD), is developed. These concepts are illustrated using a simple two-region slab model similar to that proposed by Hahm and Kulsrud--the formation of an initial shielding current sheet after perturbation by boundary rippling is calculated using MRxMHD and the final island state, after the current sheet has relaxed through a reconnection sequence, is calculated using RxMHD. Australian Research Council Grant DP110102881.
Relaxation of a 1-D gravitational system
Valageas, P
2006-01-01
We study the relaxation towards thermodynamical equilibrium of a 1-D gravitational system. This OSC model shows a series of critical energies $E_{cn}$ where new equilibria appear and we focus on the homogeneous ($n=0$), one-peak ($n=\\pm 1$) and two-peak ($n=2$) states. Using numerical simulations we investigate the relaxation to the stable equilibrium $n=\\pm 1$ of this $N-$body system starting from initial conditions defined by equilibria $n=0$ and $n=2$. We find that in a fashion similar to other long-range systems the relaxation involves a fast violent relaxation phase followed by a slow collisional phase as the system goes through a series of quasi-stationary states. Moreover, in cases where this slow second stage leads to a dynamically unstable configuration (two peaks with a high mass ratio) it is followed by a new sequence ``violent relaxation/slow collisional relaxation''. We obtain an analytical estimate of the relaxation time $t_{2\\to \\pm 1}$ through the mean escape time of a particle from its potent...
Bayesian Vector Autoregressions with Stochastic Volatility
Uhlig, H.F.H.V.S.
1996-01-01
This paper proposes a Bayesian approach to a vector autoregression with stochastic volatility, where the multiplicative evolution of the precision matrix is driven by a multivariate beta variate.Exact updating formulas are given to the nonlinear filtering of the precision matrix.Estimation of the au
Bayesian Estimation Supersedes the "t" Test
Kruschke, John K.
2013-01-01
Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional "t" tests) when certainty in the estimate is…
Bayesian Meta-Analysis of Coefficient Alpha
Brannick, Michael T.; Zhang, Nanhua
2013-01-01
The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…
Comprehension and computation in Bayesian problem solving
Directory of Open Access Journals (Sweden)
Eric D. Johnson
2015-07-01
Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.
Bayesian Networks: Aspects of Approximate Inference
Bolt, J.H.
2008-01-01
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a given problem domain. Such a network consists of an acyclic directed graph in which the nodes represent stochastic variables, supplemented with probabilities indicating the strength of the influences betw
Bayesian Benefits for the Pragmatic Researcher
Wagenmakers, E.-J.; Morey, R.D.; Lee, M.D.
2016-01-01
The practical advantages of Bayesian inference are demonstrated here through two concrete examples. In the first example, we wish to learn about a criminal’s IQ: a problem of parameter estimation. In the second example, we wish to quantify and track support in favor of the null hypothesis that Adam
Communication cost in Distributed Bayesian Belief Networks
Gosliga, S.P. van; Maris, M.G.
2005-01-01
In this paper, two different methods for information fusionare compared with respect to communication cost. These are the lambda-pi and the junction tree approach as the probability computing methods in Bayesian networks. The analysis is done within the scope of large distributed networks of computi
Decision generation tools and Bayesian inference
Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas
2014-05-01
Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.
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
Multisnapshot Sparse Bayesian Learning for DOA
DEFF Research Database (Denmark)
Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki
2016-01-01
The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source p...
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.
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,...
Bayesian Estimation of Thermonuclear Reaction Rates
Iliadis, C.; Anderson, K. S.; Coc, A.; Timmes, F. X.; Starrfield, S.
2016-11-01
The problem of estimating non-resonant astrophysical S-factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S-factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p,γ)3He, 3He(3He,2p)4He, and 3He(α,γ)7Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.
Bayesian regularization of diffusion tensor images
DEFF Research Database (Denmark)
Frandsen, Jesper; Hobolth, Asger; Østergaard, Leif;
2007-01-01
several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the three dimensional fibre bundles. In this paper we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing...
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.
Neural network classification - A Bayesian interpretation
Wan, Eric A.
1990-01-01
The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.
On local optima in learning bayesian networks
DEFF Research Database (Denmark)
Dalgaard, Jens; Kocka, Tomas; Pena, Jose
2003-01-01
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...
Automatic Thesaurus Construction Using Bayesian Networks.
Park, Young C.; Choi, Key-Sun
1996-01-01
Discusses automatic thesaurus construction and characterizes the statistical behavior of terms by using an inference network. Highlights include low-frequency terms and data sparseness, Bayesian networks, collocation maps and term similarity, constructing a thesaurus from a collocation map, and experiments with test collections. (Author/LRW)
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
Diagnosis of Subtraction Bugs Using Bayesian Networks
Lee, Jihyun; Corter, James E.
2011-01-01
Diagnosis of misconceptions or "bugs" in procedural skills is difficult because of their unstable nature. This study addresses this problem by proposing and evaluating a probability-based approach to the diagnosis of bugs in children's multicolumn subtraction performance using Bayesian networks. This approach assumes a causal network relating…
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...
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 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...
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
Bayesian Analyses of Nonhomogeneous Autoregressive Processes
1986-09-01
random coefficient autoregressive processes have a wide applicability in the analysis of economic, sociological, biological and industrial data...1980). Approximate Bayesian Methods. Trabajos Estadistica , Vol. 32, pp. 223-237. LIU, L. M. and G. C. TIAO (1980). Random Coefficient First
Face detection by aggregated Bayesian network classifiers
Pham, T.V.; Worring, M.; Smeulders, A.W.M.
2002-01-01
A face detection system is presented. A new classification method using forest-structured Bayesian networks is used. The method is used in an aggregated classifier to discriminate face from non-face patterns. The process of generating non-face patterns is integrated with the construction of the aggr
Error probabilities in default Bayesian hypothesis testing
Gu, Xin; Hoijtink, Herbert; Mulder, J,
2016-01-01
This paper investigates the classical type I and type II error probabilities of default Bayes factors for a Bayesian t test. Default Bayes factors quantify the relative evidence between the null hypothesis and the unrestricted alternative hypothesis without needing to specify prior distributions for
Most frugal explanations in Bayesian networks
Kwisthout, J.H.P.
2015-01-01
Inferring the most probable explanation to a set of variables, given a partial observation of the remaining variables, is one of the canonical computational problems in Bayesian networks, with widespread applications in AI and beyond. This problem, known as MAP, is computationally intractable (NP-ha
Modelling crime linkage with Bayesian networks
J. de Zoete; M. Sjerps; D. Lagnado; N. Fenton
2015-01-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model
Structural relaxation in dense liquids composed of anisotropic particles.
Shen, Tianqi; Schreck, Carl; Chakraborty, Bulbul; Freed, Denise E; O'Hern, Corey S
2012-10-01
We perform extensive molecular dynamics simulations of dense liquids composed of bidisperse dimer- and ellipse-shaped particles in two dimensions that interact via purely repulsive contact forces. We measure the structural relaxation times obtained from the long-time α decay of the self part of the intermediate scattering function for the translational and rotational degrees of freedom (DOF) as a function of packing fraction φ, temperature T, and aspect ratio α. We are able to collapse the packing-fraction and temperature-dependent structural relaxation times for disks, and dimers and ellipses over a wide range of α, onto a universal scaling function F(±)(|φ-φ(0)|,T,α), which is similar to that employed in previous studies of dense liquids composed of purely repulsive spherical particles in three dimensions. F(±) for both the translational and rotational DOF are characterized by the α-dependent scaling exponents μ and δ and packing fraction φ(0)(α) that signals the crossover in the scaling form F(±) from hard-particle dynamics to super-Arrhenius behavior for each aspect ratio. We find that the fragility of structural relaxation at φ(0), m(φ(0)), decreases monotonically with increasing aspect ratio for both ellipses and dimers. For α>α(p), where α(p) is the location of the peak in the packing fraction φ(J) at jamming onset, the rotational DOF are strongly coupled to the translational DOF, and the dynamic scaling exponents and φ(0) are similar for the rotational and translational DOF. For 1composed of dimer- and ellipse-shaped particles are qualitatively the same, despite the fact that zero-temperature static packings of dimers are isostatic, while static packings of ellipses are hypostatic. Thus, zero-temperature contact counting arguments do not apply to structural relaxation of dense liquids of anisotropic particles near the glass transition.
Graf, Daniel L; Jones, Hugh; Geneva, Anthony J; Pfeiffer, John M; Klunzinger, Michael W
2015-04-01
The freshwater mussel family Hyriidae (Mollusca: Bivalvia: Unionida) has a disjunct trans-Pacific distribution in Australasia and South America. Previous phylogenetic analyses have estimated the evolutionary relationships of the family and the major infra-familial taxa (Velesunioninae and Hyriinae: Hyridellini in Australia; Hyriinae: Hyriini, Castaliini, and Rhipidodontini in South America), but taxon and character sampling have been too incomplete to support a predictive classification or allow testing of biogeographical hypotheses. We sampled 30 freshwater mussel individuals representing the aforementioned hyriid taxa, as well as outgroup species representing the five other freshwater mussel families and their marine sister group (order Trigoniida). Our ingroup included representatives of all Australian genera. Phylogenetic relationships were estimated from three gene fragments (nuclear 28S, COI and 16S mtDNA) using maximum parsimony, maximum likelihood, and Bayesian inference, and we applied a Bayesian relaxed clock model calibrated with fossil dates to estimate node ages. Our analyses found good support for monophyly of the Hyriidae and the subfamilies and tribes, as well as the paraphyly of the Australasian taxa (Velesunioninae, (Hyridellini, (Rhipidodontini, (Castaliini, Hyriini)))). The Hyriidae was recovered as sister to a clade comprised of all other Recent freshwater mussel families. Our molecular date estimation supported Cretaceous origins of the major hyriid clades, pre-dating the Tertiary isolation of South America from Antarctica/Australia. We hypothesize that early diversification of the Hyriidae was driven by terrestrial barriers on Gondwana rather than marine barriers following disintegration of the super-continent.
Directory of Open Access Journals (Sweden)
Massimo Giuliani
2013-01-01
Full Text Available Introduction. The evolutionary and demographic history of the circular recombinant form CRF02_AG in a selected retrospective group of HIV-1 infected men who have sex with men (MSM resident in Central Italy was investigated. Methods. A total of 55 HIV-1 subtype CRF02_AG pol sequences were analyzed using Bayesian methods and a relaxed molecular clock to reconstruct their dated phylogeny and estimate population dynamics. Results. Dated phylogeny indicated that the HIV-1 CRF02_AG strains currently circulating in Central Italy originated in the early 90's. Bayesian phylogenetic analysis revealed the existence of a main HIV-1 CRF02_AG clade, introduced in the area of Rome before 2000 and subsequently differentiated in two different subclades with a different date of introduction (2000 versus 2005. All the sequences within clusters were interspersed, indicating that the MSM analyzed form a close and restricted network where the individuals, also moving within different clinical centers, attend the same places to meet and exchange sex. Conclusions. It was suggested that the HIV-1 CRF02_AG epidemic entered central Italy in the early 1990s, with a similar trend observed in western Europe.
Stress Relaxation in Entangled Polymer Melts
DEFF Research Database (Denmark)
Hou, Ji-Xuan; Svaneborg, Carsten; Everaers, Ralf
2010-01-01
and into the terminal relaxation regime for Z=10. Using the known (Rouse) mobility of unentangled chains and the melt entanglement length determined via the primitive path analysis of the microscopic topological state of our systems, we have performed parameter-free tests of several different tube models. We find......We present an extensive set of simulation results for the stress relaxation in equilibrium and step-strained bead-spring polymer melts. The data allow us to explore the chain dynamics and the shear relaxation modulus, G(t), into the plateau regime for chains with Z=40 entanglements...
Compact vs. Exponential-Size LP Relaxations
Energy Technology Data Exchange (ETDEWEB)
Carr, R.D.; Lancia, G.
2000-09-01
In this paper we introduce by means of examples a new technique for formulating compact (i.e. polynomial-size) LP relaxations in place of exponential-size models requiring separation algorithms. In the same vein as a celebrated theorem by Groetschel, Lovasz and Schrijver, we state the equivalence of compact separation and compact optimization. Among the examples used to illustrate our technique, we introduce a new formulation for the Traveling Salesman Problem, whose relaxation we show equivalent to the subtour elimination relaxation.
Spin relaxation in nanowires by hyperfine coupling
Energy Technology Data Exchange (ETDEWEB)
Echeverria-Arrondo, C. [Department of Physical Chemistry, Universidad del Pais Vasco UPV/EHU, 48080 Bilbao (Spain); Sherman, E.Ya. [Department of Physical Chemistry, Universidad del Pais Vasco UPV/EHU, 48080 Bilbao (Spain); IKERBASQUE Basque Foundation for Science, 48011 Bilbao, Bizkaia (Spain)
2012-08-15
Hyperfine interactions establish limits on spin dynamics and relaxation rates in ensembles of semiconductor quantum dots. It is the confinement of electrons which determines nonzero hyperfine coupling and leads to the spin relaxation. As a result, in nanowires one would expect the vanishing of this effect due to extended electron states. However, even for relatively clean wires, disorder plays a crucial role and makes electron localization sufficient to cause spin relaxation on the time scale of the order of 10 ns. (copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Le Chatelier's principle with multiple relaxation channels
Gilmore, R.; Levine, R. D.
1986-05-01
Le Chatelier's principle is discussed within the constrained variational approach to thermodynamics. The formulation is general enough to encompass systems not in thermal (or chemical) equilibrium. Particular attention is given to systems with multiple constraints which can be relaxed. The moderation of the initial perturbation increases as additional constraints are removed. This result is studied in particular when the (coupled) relaxation channels have widely different time scales. A series of inequalities is derived which describes the successive moderation as each successive relaxation channel opens up. These inequalities are interpreted within the metric-geometry representation of thermodynamics.
Neural control of muscle relaxation in echinoderms.
Elphick, M R; Melarange, R
2001-03-01
Smooth muscle relaxation in vertebrates is regulated by a variety of neuronal signalling molecules, including neuropeptides and nitric oxide (NO). The physiology of muscle relaxation in echinoderms is of particular interest because these animals are evolutionarily more closely related to the vertebrates than to the majority of invertebrate phyla. However, whilst in vertebrates there is a clear structural and functional distinction between visceral smooth muscle and skeletal striated muscle, this does not apply to echinoderms, in which the majority of muscles, whether associated with the body wall skeleton and its appendages or with visceral organs, are made up of non-striated fibres. The mechanisms by which the nervous system controls muscle relaxation in echinoderms were, until recently, unknown. Using the cardiac stomach of the starfish Asterias rubens as a model, it has been established that the NO-cGMP signalling pathway mediates relaxation. NO also causes relaxation of sea urchin tube feet, and NO may therefore function as a 'universal' muscle relaxant in echinoderms. The first neuropeptides to be identified in echinoderms were two related peptides isolated from Asterias rubens known as SALMFamide-1 (S1) and SALMFamide-2 (S2). Both S1 and S2 cause relaxation of the starfish cardiac stomach, but with S2 being approximately ten times more potent than S1. SALMFamide neuropeptides have also been isolated from sea cucumbers, in which they cause relaxation of both gut and body wall muscle. Therefore, like NO, SALMFamides may also function as 'universal' muscle relaxants in echinoderms. The mechanisms by which SALMFamides cause relaxation of echinoderm muscle are not known, but several candidate signal transduction pathways are discussed here. The SALMFamides do not, however, appear to act by promoting release of NO, and muscle relaxation in echinoderms is therefore probably regulated by at least two neuronal signalling systems acting in parallel. Recently, other
Nuclear magnetic resonance relaxation in multiple sclerosis
DEFF Research Database (Denmark)
Larsson, H B; Barker, G J; MacKay, A
1998-01-01
OBJECTIVES: The theory of relaxation processes and their measurements are described. An overview is presented of the literature on relaxation time measurements in the normal and the developing brain, in experimental diseases in animals, and in patients with multiple sclerosis. RESULTS...... AND CONCLUSION: Relaxation time measurements provide insight into development of multiple sclerosis plaques, especially the occurrence of oedema, demyelination, and gliosis. There is also evidence that normal appearing white matter in patients with multiple sclerosis is affected. What is now needed are fast...
Hydrogen sulfide and vascular relaxation
Institute of Scientific and Technical Information of China (English)
SUN Yan; TANG Chao-shu; DU Jun-bao; JIN Hong-fang
2011-01-01
Objective To review the vasorelaxant effects of hydrogen sulfide (H2S) in arterial rings in the cardiovascular system under both physiological and pathophysiological conditions and the possible mechanisms involved.Data sources The data in this review were obtained from Medline and Pubmed sources from 1997 to 2011 using the search terms "hydrogen sulfide" and ""vascular relaxation".Study selection Articles describing the role of hydrogen sulfide in the regulation of vascular activity and its vasorelaxant effects were selected.Results H2S plays an important role in the regulation of cardiovascular tone.The vasomodulatory effects of H2S depend on factors including concentration,species and tissue type.The H2S donor,sodium hydrosulfide (NarS),causes vasorelaxation of rat isolated aortic rings in a dose-dependent manner.This effect was more pronounced than that observed in pulmonary arterial rings.The expression of KATP channel proteins and mRNA in the aortic rings was increased compared with pulmonary artery rings.H2S is involved in the pathogenesis of a variety of cardiovascular diseases.Downregulation of the endogenous H2S pathway is an important factor in the pathogenesis of cardiovascular diseases.The vasorelaxant effects of H2S have been shown to be mediated by activation of KATP channels in vascular smooth muscle cells and via the induction of acidification due to activation of the CI/HCO3 exchanger.It is speculated that the mechanisms underlying the vasoconstrictive function of H2S in the aortic rings involves decreased NO production and inhibition of cAMP accumulation.Conclusion H2S is an important endogenous gasotransmitter in the cardiovascular system and acts as a modulator of vascular tone in the homeostatic regulation of blood pressure.
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.
Bayesian structural equation modeling in sport and exercise psychology.
Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus
2015-08-01
Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.
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...
“I think relax, relax and it flows a lot easier”: Exploring client-generated relax strategies
Directory of Open Access Journals (Sweden)
Dianne Cirone
2014-10-01
Full Text Available Background. Some adult stroke survivors participating in Cognitive Orientation to daily Occupational Performance (CO-OP treatment programs self-generated relax strategies that have not been explored in previous CO-OP publications. The objective of this study was to describe the process by which adults with stroke used relax strategies and to explore the outcomes associated with their use. Methods. Secondary analysis of transcripts of intervention sessions from five participants was conducted. Results. All five participants applied relax strategies after initially observing a breakdown in performance that was attributed to increased fatigue or tension. The relax strategies used by the participants during their occupations included general relaxation, physical modifications to reduce tension, mental preparation, and pacing. The application of these strategies seemed to result in improved skill performance, reduced fatigue, and transfer to other activities. Conclusion. The relax strategy warrants further investigation as a potentially important therapeutic tool to improve occupational performance in individuals who have had a stroke.
STUDIES ON ULTRASONIC ABSORPTION AND VISCO-RELAXATION OF POLY (ETHYLENE GLYCOL) AQUEOUS SOLUTIONS
Institute of Scientific and Technical Information of China (English)
SUBHI KEMAL HASSUN; ALIA ABDULMUHSIN SHIHAB; FATIMA ABDULAMIR JASSIM
1989-01-01
Ultrasonic absorption and velocity measurements were made on aqueous solutions of poly (ethylene glycol)(PEG)of different molecular weights and concentrations, using a pulse sender-receiver ultrasonic generator, Measurements were obtained at a frequency of 2MHz., and a temperature of 293 K. The results show a linear increase of the Values of velocity, density and viscosity with increase ofmolecu lar weight and concentration of PEG. On the contrary, the attenuation values decreased with increase of molecular weight and concentration of PEG. A mathematical equation correlating relaxation amplitude and molecular weight of the polymer is suggested. This was applied to calculate the molecular weights of unknown samples of PEG from their measured relaxation amplitude. The results obtained were in good agreement with those obtained from osmometry.
Stress relaxation of narrow molar mass distribution polystyrene following uniaxial extension
DEFF Research Database (Denmark)
Nielsen, Jens Kromann; Rasmussen, Henrik K.; Hassager, Ole
2008-01-01
The stress in the startup of uniaxial elongational flow until steady state, followed by stress relaxation, has been measured for a narrow molar mass distribution polystyrene inelt with a molecular weight of 145 kg/mol. The experiments are conducted on a filament stretching rheometer, where a clos...... rates. (C) 2008 The Society of Rheology....
Ultrafast Librational Relaxation of H2O in Liquid Water
DEFF Research Database (Denmark)
Petersen, Jakob; Møller, Klaus Braagaard; Rey, Rossend
2013-01-01
The ultrafast librational (hindered rotational) relaxation of a rotationally excited H2O molecule in pure liquid water is investigated by means of classical nonequilibrium molecular dynamics simulations and a power and work analysis. This analysis allows the mechanism of the energy transfer from...... the excited H2O to its water neighbors, which occurs on a sub-100 fs time scale, to be followed in molecular detail, i.e., to determine which water molecules receive the energy and in which degrees of freedom. It is found that the dominant energy flow is to the four hydrogen-bonded water partners in the first...... hydration shell, dominated by those partners’ rotational motion, in a fairly symmetric fashion over the hydration shell. The minority component of the energy transfer, to these neighboring waters’ translational motion, exhibits an asymmetry in energy reception between hydrogen-bond-donating and -accepting...
Two-Body Relaxation in Cosmological Simulations
Binney, J; Binney, James; Knebe, Alexander
2002-01-01
The importance of two-body relaxation in cosmological simulations is explored with simulations in which there are two species of particles. The cases of mass ratio sqrt(2):1 and 4:1 are investigated. Simulations are run with both a fixed softening length and adaptive softening using the publicly available codes GADGET and MLAPM, respectively. The effects of two-body relaxation are detected in both the density profiles of halos and the mass function of halos. The effects are more pronounced with a fixed softening length, but even in this case they are not so large as to suggest that results obtained with one mass species are significantly affected by two-body relaxation. The simulations that use adaptive softening are slightly less affected by two-body relaxation and produce slightly higher central densities in the largest halos. They run about three times faster than the simulations that use a fixed softening length.
Energy landscape of relaxed amorphous silicon
Valiquette, Francis; Mousseau, Normand
2003-09-01
We analyze the structure of the energy landscape of a well-relaxed 1000-atom model of amorphous silicon using the activation-relaxation technique (ART nouveau). Generating more than 40 000 events starting from a single minimum, we find that activated mechanisms are local in nature, that they are distributed uniformly throughout the model, and that the activation energy is limited by the cost of breaking one bond, independently of the complexity of the mechanism. The overall shape of the activation-energy-barrier distribution is also insensitive to the exact details of the configuration, indicating that well-relaxed configurations see essentially the same environment. These results underscore the localized nature of relaxation in this material.
Stress Relaxation in Entangled Polymer Melts
DEFF Research Database (Denmark)
Hou, Ji-Xuan; Svaneborg, Carsten; Everaers, Ralf;
2010-01-01
We present an extensive set of simulation results for the stress relaxation in equilibrium and step-strained bead-spring polymer melts. The data allow us to explore the chain dynamics and the shear relaxation modulus, G(t), into the plateau regime for chains with Z=40 entanglements and into the t......We present an extensive set of simulation results for the stress relaxation in equilibrium and step-strained bead-spring polymer melts. The data allow us to explore the chain dynamics and the shear relaxation modulus, G(t), into the plateau regime for chains with Z=40 entanglements...... excellent agreement for the Likhtman-McLeish theory using the double reptation approximation for constraint release, if we remove the contribution of high-frequency modes to contour length fluctuations of the primitive chain....
Precession Relaxation of Viscoelastic Oblate Rotators
Frouard, Julien
2016-01-01
Various perturbations (collisions, close encounters, YORP) destabilise the rotation of a small body, leaving it in a non-principal spin state. Then the body experiences alternating stresses generated by the inertial forces. The ensuing inelastic dissipation reduces the kinetic energy, without influencing the angular momentum. This yields nutation relaxation, i.e., evolution of the spin towards rotation about the maximal-inertia axis. Knowledge of the timescales needed to damp the nutation is crucial in studies of small bodies' dynamics. In the past, nutation relaxation has been described by an empirical quality factor introduced to parameterise the dissipation rate and to evade the discussion of the actual rheological parameters and their role in dissipation. This approach is unable to describe the dependence of the relaxation rate upon the nutation angle, because we do not know the quality factor's dependence on the frequency (which is a function of the nutation angle). This leaves open the question of relax...
Slow spin relaxation in dipolar spin ice.
Orendac, Martin; Sedlakova, Lucia; Orendacova, Alzbeta; Vrabel, Peter; Feher, Alexander; Pajerowski, Daniel M.; Cohen, Justin D.; Meisel, Mark W.; Shirai, Masae; Bramwell, Steven T.
2009-03-01
Spin relaxation in dipolar spin ice Dy2Ti2O7 and Ho2Ti2O7 was investigated using the magnetocaloric effect and susceptibility. The magnetocaloric behavior of Dy2Ti2O7 at temperatures where the orientation of spins is governed by ``ice rules`` (T Tice) revealed thermally activated relaxation; however, the resulting temperature dependence of the relaxation time is more complicated than anticipated by a mere extrapolation of the corresponding high temperature data [1]. A susceptibility study of Ho2Ti2O7 was performed at T > Tice and in high magnetic fields, and the results suggest a slow relaxation of spins analogous to the behavior reported in a highly polarized cooperative paramagnet [2]. [1] J. Snyder et al., Phys. Rev. Lett. 91 (2003) 107201. [2] B. G. Ueland et al., Phys. Rev. Lett. 96 (2006) 027216.
Molecular mobility in glassy dispersions
Mehta, Mehak; McKenna, Gregory B.; Suryanarayanan, Raj
2016-05-01
Dielectric spectroscopy was used to characterize the structural relaxation in pharmaceutical dispersions containing nifedipine (NIF) and either poly(vinyl) pyrrolidone (PVP) or hydroxypropyl methylcellulose acetate succinate (HPMCAS). The shape of the dielectric response (permittivity versus log time) curve was observed to be independent of temperature. Thus, for the pure NIF as well as the dispersions, the validity of the time-temperature superposition principle was established. Furthermore, though the shape of the full dielectric response varied with polymer concentration, the regime related to the α- or structural relaxation was found to superimpose for the dispersions, though not with the response of the NIF itself. Hence, there is a limited time-temperature-concentration superposition for these systems as well. Therefore, in this polymer concentration range, calculation of long relaxation times in these glass-forming systems becomes possible. We found that strong drug-polymer hydrogen bonding interactions improved the physical stability (i.e., delayed crystallization) by reducing the molecular mobility. The strength of hydrogen bonding, structural relaxation time, and crystallization followed the order: NIF-PV P>NIF-HPMCAS>NIF. With an increase in polymer concentration, the relaxation times were longer indicating a decrease in molecular mobility. The temperature dependence of relaxation time, in other words fragility, was independent of polymer concentration. This is the first application of the superposition principle to characterize structural relaxation in glassy pharmaceutical dispersions.