Likelihood-free methods for cognitive science /: James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.

Palestro, James J.,; Sederberg, Peter C.,; Osth, Adam F.,; Van Zandt, Trisha,; Turner, Brandon M.,

doi:10.1007/978-3-319-72425-6

Likelihood-free methods for cognitive science / James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.

Palestro, James J., author.; Sederberg, Peter C., 1943- author.; Osth, Adam F., author.; Van Zandt, Trisha, author.; Turner, Brandon M., author.

2018

BF311

Available Online

Formats

Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS

Add to Basket

Cite

Linked e-resources

Linked Resource

Online Access

Concurrent users

Unlimited

Authorized users

Document Delivery Supplied

Can lend chapters, not whole ebooks

Details

Title

Likelihood-free methods for cognitive science / James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.

Author

Palestro, James J., author.

ISBN

9783319724256 (electronic book)
3319724258 (electronic book)
9783319724249
331972424X

DOI

https://doi.org/10.1007/978-3-319-72425-6

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.

Access Note

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

Sederberg, Peter C., 1943- author.
Osth, Adam F., author.
Van Zandt, Trisha, author.
Turner, Brandon M., author.

Series

Computational Approaches to Cognition and Perception.