About this book
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.
- Book Title Likelihood-Free Methods for Cognitive Science
- Series Title Computational Approaches to Cognition and Perception
- Series Abbreviated Title Computational Approaches to Cognition and Perception
- DOI https://doi.org/10.1007/978-3-319-72425-6
- Copyright Information Springer International Publishing AG 2018
- Publisher Name Springer, Cham
- eBook Packages Behavioral Science and Psychology Behavioral Science and Psychology (R0)
- Hardcover ISBN 978-3-319-72424-9
- Softcover ISBN 978-3-319-89181-1
- eBook ISBN 978-3-319-72425-6
- Series ISSN 2510-1889
- Series E-ISSN 2510-1897
- Edition Number 1
- Number of Pages XIV, 129
- Number of Illustrations 20 b/w illustrations, 7 illustrations in colour
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