Bayesian analysis of simulation-based models

[1]  D. Andrew Brown,et al.  ROBERT E. KASS , URI T. EDEN , EMERY N. BROWN . Analysis of Neural Data . New York : Springer Science + Business Media , 2017 .

[2]  Jean-Marie Cornuet,et al.  ABC model choice via random forests , 2014 .

[3]  Brandon M. Turner,et al.  A generalized, likelihood-free method for posterior estimation , 2014, Psychonomic bulletin & review.

[4]  Brandon M. Turner,et al.  Hierarchical Approximate Bayesian Computation , 2014, Psychometrika.

[5]  Emery N. Brown,et al.  Analysis of Neural Data , 2014 .

[6]  Birte U. Forstmann,et al.  A Bayesian framework for simultaneously modeling neural and behavioral data , 2013, NeuroImage.

[7]  Brandon M. Turner,et al.  A method for efficiently sampling from distributions with correlated dimensions. , 2013, Psychological methods.

[8]  Brandon M. Turner,et al.  Likelihood-free Bayesian analysis of memory models. , 2013, Psychological review.

[9]  Jukka Corander,et al.  Approximate Bayesian Computation , 2013, PLoS Comput. Biol..

[10]  Marius Usher,et al.  Disentangling decision models: from independence to competition. , 2013, Psychological review.

[11]  Brandon M. Turner,et al.  Approximate Bayesian computation with differential evolution , 2012 .

[12]  Brandon M. Turner,et al.  A tutorial on approximate Bayesian computation , 2012 .

[13]  Han L J van der Maas,et al.  Optimal decision making in neural inhibition models. , 2012, Psychological review.

[14]  E. Wagenmakers,et al.  The Speed-Accuracy Tradeoff in the Elderly Brain: A Structural Model-Based Approach , 2011, The Journal of Neuroscience.

[15]  Christian P Robert,et al.  Lack of confidence in approximate Bayesian computation model choice , 2011, Proceedings of the National Academy of Sciences.

[16]  R. Sinha,et al.  Body Mass Index and Diabetes in Asia: A Cross-Sectional Pooled Analysis of 900,000 Individuals in the Asia Cohort Consortium , 2011, PloS one.

[17]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[18]  Marius Usher,et al.  Testing Multi-Alternative Decision Models with Non-Stationary Evidence , 2011, Front. Neurosci..

[19]  James L. McClelland,et al.  Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making , 2011, PloS one.

[20]  Richard G. Everitt,et al.  Likelihood-free estimation of model evidence , 2011 .

[21]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[22]  Michael P. H. Stumpf,et al.  Simulation-based model selection for dynamical systems in systems and population biology , 2009, Bioinform..

[23]  By W. R. GILKSt,et al.  Adaptive Rejection Sampling for Gibbs Sampling , 2010 .

[24]  Andrew Heathcote,et al.  Getting more from accuracy and response time data: Methods for fitting the linear ballistic accumulator , 2009, Behavior research methods.

[25]  A. Heathcote,et al.  Is the Linear Ballistic Accumulator Model Really the Simplest Model of Choice Response Times: A Bayesian Model Complexity Analysis , 2009 .

[26]  David Welch,et al.  Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems , 2009, Journal of The Royal Society Interface.

[27]  Scott D. Brown,et al.  The simplest complete model of choice response time: Linear ballistic accumulation , 2008, Cognitive Psychology.

[28]  C. Robert,et al.  ABC likelihood-free methods for model choice in Gibbs random fields , 2008, 0807.2767.

[29]  Jean-Michel Marin,et al.  On some difficulties with a posterior probability approximation technique , 2008 .

[30]  Marius Usher,et al.  Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  T. Ando Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models , 2007 .

[32]  Jay I. Myung,et al.  Analytic Expressions for the BCDMEM Model of Recognition Memory. , 2007, Journal of mathematical psychology.

[33]  Tony O’Hagan Bayes factors , 2006 .

[34]  C. Robert,et al.  Deviance information criteria for missing data models , 2006 .

[35]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[36]  Cajo J. F. ter Braak,et al.  A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces , 2006, Stat. Comput..

[37]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[38]  E. Flekkøy,et al.  Hybrid computations with flux exchange , 2004, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[39]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[40]  Jeffrey N. Rouder,et al.  A hierarchical bayesian statistical framework for response time distributions , 2003 .

[41]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[42]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[43]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

[44]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[45]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[46]  T. Zandt,et al.  How to fit a response time distribution , 2000, Psychonomic bulletin & review.

[47]  D. Weakliem A Critique of the Bayesian Information Criterion for Model Selection , 1999 .

[48]  Arthur P. Dempster,et al.  The direct use of likelihood for significance testing , 1997, Stat. Comput..

[49]  L. Wasserman,et al.  Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .

[50]  W. Gilks,et al.  Adaptive Rejection Metropolis Sampling Within Gibbs Sampling , 1995 .

[51]  R. Kass,et al.  Approximate Bayes Factors and Orthogonal Parameters, with Application to Testing Equality of Two Binomial Proportions , 1992 .

[52]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[53]  L. Tierney,et al.  Accurate Approximations for Posterior Moments and Marginal Densities , 1986 .

[54]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[55]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[56]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[57]  V. A. Epanechnikov Non-Parametric Estimation of a Multivariate Probability Density , 1969 .

[58]  HighWire Press Philosophical Transactions of the Royal Society of London , 1781, The London Medical Journal.