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Likelihood-free methods for cognitiv...

Palestro, James J.

Likelihood-free methods for cognitive science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Likelihood-free methods for cognitive scienceby James J. Palestro ... [et al.].
other author:	Palestro, James J.
Published:	Cham :Springer International Publishing :2018.
Description:	xiv, 129 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Cognitive scienceMethodology.
Online resource:	http://dx.doi.org/10.1007/978-3-319-72425-6
ISBN:	9783319724256$q(electronic bk.)

Likelihood-free methods for cognitive science
Likelihood-free methods for cognitive science[electronic resource] /by James J. Palestro ... [et al.]. - Cham :Springer International Publishing :2018. - xiv, 129 p. :ill., digital ;24 cm. - Computational approaches to cognition and perception,2510-1889. - Computational approaches to cognition and perception..

Chapter 1. Motivation -- Chapter 2. Likelihood-Free Algorithms -- Chapter 3. A Tutorial -- Chapter 4. Validations -- Chapter 5. Applications -- Chapter 6. Conclusions -- Chapter 7. Distributions.

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.

ISBN: 9783319724256$q(electronic bk.)

Standard No.: 10.1007/978-3-319-72425-6doiSubjects--Topical Terms:

544978
Cognitive science
--Methodology.

LC Class. No.: BF311

Dewey Class. No.: 153

Likelihood-free methods for cognitive science
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245 0 0 $a Likelihood-free methods for cognitive science $h [electronic resource] / $c by James J. Palestro ... [et al.].
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300 $a xiv, 129 p. : $b ill., digital ; $c 24 cm.
490 1 $a Computational approaches to cognition and perception, $x 2510-1889
505 0 $a Chapter 1. Motivation -- Chapter 2. Likelihood-Free Algorithms -- Chapter 3. A Tutorial -- Chapter 4. Validations -- Chapter 5. Applications -- Chapter 6. Conclusions -- Chapter 7. Distributions.
520 $a 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.
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650 2 4 $a Cognitive Psychology. $3 273717
700 1 $a Palestro, James J. $3 806126
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