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Likelihood-free methods for cognitive science / James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.

9783319724256 (electronic book)
3319724258 (electronic book)
9783319724249
331972424X

Cham : Springer, 2018.

English

1 online resource.

10.1007/978-3-319-72425-6 doi

BF311

153

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.

Includes bibliographical references and index.

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text file PDF

Online resource; title from PDF title page (viewed February 20, 2018).

Computational Approaches to Cognition and Perception.

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