This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function. As a result, ABC can be used to estimate posterior distributions of parameters for simulation-based models. Simulation-based models are now very popular in cognitive science, as are Bayesian methods for performing parameter inference. As such, the recent developments of likelihood-free techniques are an important advancement for the field.

Chapters discuss the philosophy of Bayesian inference as well as provide several algorithms for performing ABC. Chapters also apply some of the algorithms in a tutorial fashion, with one specific application to the Minerva 2 model. In addition, the book discusses several applications of ABC methodology to recent problems in cognitive science.

Likelihood-Free Methods for Cognitive Science will be of interest to researchers and graduate students working in experimental, applied, and cognitive science.

Provides an in-depth foundation of approximate Bayesian analysis (ABC) Offers a tutorial demonstration of a popular model in cognitive science that can be readily adapted to other models Reviews many of the most prominent algorithms for performing likelihood-free Bayesian analysis

Autorentext

James J. Palestro is a doctoral student in the Psychology Department at The Ohio State University. He received a B.A. from Youngstown State University in psychology in 2012 and a M.A. from the Ohio State University in 2017. His research interests include cognitive modeling and the neural bases of perceptual decision making.

Per B. Sederberg is an Associate Professor in the Department of Psychology at the University of Virginia. He received his undergraduate degree in Cognitive Science from the University of Virginia in 1996. He then worked as a computer programmer in industry before returning to earn his Ph.D. in Neuroscience from the University of Pennsylvania in 2006. He spent four years as a postdoctoral fellow at Princeton University where he studied machine-learning applications to the study of the brain. In 2010 he began as an Assistant Professor at The Ohio State University and moved to the University of Virginia in the summer of 2017. As a computational cognitive neuroscientist, his research seeks to develop a comprehensive mathematical understanding of cognition, with a particular focus on human memory and decision-making, that links neuroscience and behavior.

Adam F. Osth is a Lecturer in the Melbourne School of Psychological Sciences at the University of Melbourne. He received a B.A. in psychology from the University of California Santa Cruz, and both an M. A. in 2011 and Ph D in 2014 in Psychology from The Ohio State University. He spent two years as a postdoctoral fellow at The University of Newcastle in New South Wales, Australia. His research focuses on both empirical and computational modeling investigations of episodic memory, with focuses in recognition memory, serial order memory, and the integration of models of memory and decision making.

Trisha Van Zandt is a professor of Psychology at The Ohio State University. She is a member of the Society for Mathematical Psychology, of which she wasPresident in 2006-2007, and the American Statistical Association. She has received multiple research grants from the National Science Foundation and the Presidential Early Career Award for Scientists and Engineers in 1997. She is co-author of review chapters Designs for and Analyses of Response Time Experiments in the Oxford Handbook of Quantitative Methods and Mathematical Psychology in the APA Handbook of Research Methods in Psycholog y.

Brandon M. Turner is an Assistant Professor in the Psychology Department at The Ohio State University. He received a B.S. from Missouri State University in mathematics and psychology in 2008, a MAS from The Ohio State University in statistics in 2010, and a Ph.D. from The Ohio State University in 2011. He then spent one year as a postdoctoral researcher at University of California, Irvine, and two years as a postdoctoral fellow at Stanford University. His research interests include dynamic models of cognition and perceptual decision making, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition.

Inhalt
Chapter 1. Motivation.- Chapter 2. Likelihood-Free Algorithms.- Chapter 3. A Tutorial.- Chapter 4. Validations.- Chapter 5. Applications.- Chapter 6. Conclusions.- Chapter 7. Distributions.