國立虎尾科技大學 |

Likelihood-Free Methods for Cognitive Science

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Likelihood-Free Methods for Cognitive Science/ by James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.
Author:	Palestro, James J.
other author:	Sederberg, Per B.
Description:	XIV, 129 p. 27 illus., 7 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Cognitive psychology. -
Online resource:	https://doi.org/10.1007/978-3-319-72425-6
ISBN:	9783319724256

Likelihood-Free Methods for Cognitive Science
Palestro, James J.

Likelihood-Free Methods for Cognitive Science[electronic resource] /by James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner. - 1st ed. 2018. - XIV, 129 p. 27 illus., 7 illus. in color.online resource. - 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:

556029
Cognitive psychology.

LC Class. No.: BF201

Dewey Class. No.: 153

Likelihood-Free Methods for Cognitive Science
LDR:02735nam a22004095i 4500 001 989618
003 DE-He213
005 20200704154050.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319724256 $9 978-3-319-72425-6
024 7 $a 10.1007/978-3-319-72425-6 $2 doi
035 $a 978-3-319-72425-6
050 4 $a BF201
072 7 $a JMR $2 bicssc
072 7 $a PSY008000 $2 bisacsh
072 7 $a JMR $2 thema
082 0 4 $a 153 $2 23
100 1 $a Palestro, James J. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1281465
245 1 0 $a Likelihood-Free Methods for Cognitive Science $h [electronic resource] / $c by James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIV, 129 p. 27 illus., 7 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 psychology. $3 556029
650 1 4 $a Cognitive Psychology. $3 593892
700 1 $a Sederberg, Per B. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1281466
700 1 $a Osth, Adam F. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1281467
700 1 $a Van Zandt, Trisha. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 716389
700 1 $a Turner, Brandon M. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1281468
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319724249
776 0 8 $i Printed edition: $z 9783319724263
776 0 8 $i Printed edition: $z 9783319891811
830 0 $a Computational Approaches to Cognition and Perception, $x 2510-1889 $3 1281292
856 4 0 $u https://doi.org/10.1007/978-3-319-72425-6
912 $a ZDB-2-BSP
912 $a ZDB-2-SXBP
950 $a Behavioral Science and Psychology (SpringerNature-41168)
950 $a Behavioral Science and Psychology (R0) (SpringerNature-43718)