國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁 到查詢結果 [ subject:"Cognitive Psychology." ]

切換: 標籤 | MARC模式 | ISBD

Likelihood-free methods for cognitiv...

Palestro, James J.

Likelihood-free methods for cognitive science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Likelihood-free methods for cognitive scienceby James J. Palestro ... [et al.].
其他作者:	Palestro, James J.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xiv, 129 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Cognitive scienceMethodology.
電子資源:	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
LDR:02241nmm a2200325 a 4500 001 531584
003 DE-He213
005 20180821102144.0
006 m d
007 cr nn 008maaau
008 181113s2018 gw s 0 eng d
020 $a 9783319724256$q(electronic bk.)
020 $a 9783319724249$q(paper)
024 7 $a 10.1007/978-3-319-72425-6 $2 doi
035 $a 978-3-319-72425-6
040 $a GP $c GP
041 0 $a eng
050 4 $a BF311
072 7 $a JMR $2 bicssc
072 7 $a PSY008000 $2 bisacsh
082 0 4 $a 153 $2 23
090 $a BF311 $b .L727 2018
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 544978
650 1 4 $a Psychology. $3 181533
650 2 4 $a Cognitive Psychology. $3 273717
700 1 $a Palestro, James J. $3 806126
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Computational approaches to cognition and perception. $3 806118
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-72425-6
950 $a Behavioral Science and Psychology (Springer-41168)