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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.?
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 l*
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