Description
Likelihood-Free Methods for Cognitive Science, Softcover reprint of the original 1st ed. 2018
Computational Approaches to Cognition and Perception Series
Authors: Palestro James J., Sederberg Per B., Osth Adam F., Van Zandt Trisha, Turner Brandon M.
Language: English
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the book of Palestro James J., Sederberg Per B., Osth Adam F., Van Zandt Trisha, Turner Brandon M.
Publication date: 06-2019
129 p. · 15.5x23.5 cm · Paperback
52.74 €
In Print (Delivery period: 15 days).
Add to cart the print on demand of Palestro James J., Sederberg Per B., Osth Adam F., Van Zandt Trisha, Turner Brandon M.
Publication date: 02-2018
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Description
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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 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 wasPresid