Likelihood-free methods for cognitive science / James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.
2018
BF311
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Title
Likelihood-free methods for cognitive science / James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.
Author
ISBN
9783319724256 (electronic book)
3319724258 (electronic book)
9783319724249
331972424X
3319724258 (electronic book)
9783319724249
331972424X
Published
Cham : Springer, 2018.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-319-72425-6 doi
Call Number
BF311
Dewey Decimal Classification
153
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (viewed February 20, 2018).
Added Author
Series
Computational Approaches to Cognition and Perception.
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Table of Contents
Chapter 1. Motivation
Chapter 2. Likelihood-Free Algorithms
Chapter 3. A Tutorial
Chapter 4. Validations
Chapter 5. Applications
Chapter 6. Conclusions
Chapter 7. Distributions.
Chapter 2. Likelihood-Free Algorithms
Chapter 3. A Tutorial
Chapter 4. Validations
Chapter 5. Applications
Chapter 6. Conclusions
Chapter 7. Distributions.