Types of approximation for probabilistic cognition: Sampling and variational

[1]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[2]  A. Tversky,et al.  Causal Schemata in Judgments under Uncertainty , 1982 .

[3]  D. Shanks.  Forward and Backward Blocking in Human Contingency Judgement , 1985 .

[4]  D. Medin,et al.  Problem structure and the use of base-rate information from experience. , 1988, Journal of experimental psychology. General.

[5]  John R. Anderson,et al.  The Adaptive Nature of Human Categorization. , 1991 .

[6]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[7]  J. Kruschke.  Base rates in category learning. , 1996, Journal of experimental psychology. Learning, memory, and cognition.

[8]  Tom Minka,et al.  A family of algorithms for approximate Bayesian inference , 2001 .

[9]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[11]  Konrad Paul Kording,et al.  Bayesian integration in sensorimotor learning , 2004, Nature.

[12]  Michael S Landy,et al.  Combining Priors and Noisy Visual Cues in a Rapid Pointing Task , 2006, The Journal of Neuroscience.

[13]  J. Kruschke.  Locally Bayesian learning with applications to retrospective revaluation and highlighting. , 2006, Psychological review.

[14]  J. Kruschke.  Locally Bayesian Learning , 2006 .

[15]  Aaron C. Courville,et al.  The pigeon as particle filter , 2007, NIPS 2007.

[16]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[17]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[18]  Thomas L. Griffiths,et al.  Modeling the effects of memory on human online sentence processing with particle filters , 2008, NIPS.

[19]  Karl J. Friston.  Hierarchical Models in the Brain , 2008, PLoS Comput. Biol..

[20]  Iris van Rooij,et al.  The Tractable Cognition Thesis , 2008, Cogn. Sci..

[21]  Charles Kemp,et al.  The discovery of structural form , 2008, Proceedings of the National Academy of Sciences.

[22]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[23]  Thomas L. Griffiths,et al.  Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling , 2009, NIPS.

[24]  Scott D. Brown,et al.  Detecting and predicting changes , 2009, Cognitive Psychology.

[25]  P. Berkes,et al.  Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .

[26]  Adam N Sanborn,et al.  Exemplar models as a mechanism for performing Bayesian inference , 2010, Psychonomic bulletin & review.

[27]  Adam N Sanborn,et al.  Rational approximations to rational models: alternative algorithms for category learning. , 2010, Psychological review.

[28]  Radford M. Neal.  Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .

[29]  Thomas L. Griffiths,et al.  Exploring the influence of particle filter parameters on order effects in causal learning , 2011, CogSci.

[30]  D. Knill,et al.  Bayesian sampling in visual perception , 2011, Proceedings of the National Academy of Sciences.

[31]  Wolfgang Maass,et al.  Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons , 2011, PLoS Comput. Biol..

[32]  Alexandre Pouget,et al.  Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models , 2012, NIPS.

[33]  Peter Dayan,et al.  Semi-rational models of conditioning: The case of trial order , 2012 .

[34]  Thomas L. Griffiths,et al.  "Burn-in, bias, and the rationality of anchoring" , 2012, NIPS.

[35]  Joshua B. Tenenbaum,et al.  Multistability and Perceptual Inference , 2012, Neural Computation.

[36]  Ricardo Silva,et al.  Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning , 2013 .

[37]  Rajesh P. N. Rao,et al.  Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks , 2014, NIPS.

[38]  Robert A. Legenstein,et al.  Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment , 2014, PLoS Comput. Biol..

[39]  Guillaume Hennequin,et al.  Fast Sampling-Based Inference in Balanced Neuronal Networks , 2014, NIPS.

[40]  Johannes Schemmel,et al.  Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons , 2014, Front. Comput. Neurosci..