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Likelihood-free methods for cognitiv...

Palestro, James J.

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Likelihood-free methods for cognitive science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Likelihood-free methods for cognitive science/ by James J. Palestro ... [et al.].
other author:	Palestro, James J.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xiv, 129 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Motivation -- Chapter 2. Likelihood-Free Algorithms -- Chapter 3. A Tutorial -- Chapter 4. Validations -- Chapter 5. Applications -- Chapter 6. Conclusions -- Chapter 7. Distributions.
Contained By:	Springer eBooks
Subject:	Cognitive science - Methodology. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-72425-6
ISBN:	9783319724256

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

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

3301463
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.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
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.
650 0 $a Cognitive science $x Methodology. $3 3301463
650 1 4 $a Psychology. $3 519075
650 2 4 $a Cognitive Psychology. $3 891220
700 1 $a Palestro, James J. $3 3301462
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830 0 $a Computational approaches to cognition and perception. $3 3301454
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950 $a Behavioral Science and Psychology (Springer-41168)