Advertisement

Likelihood-Free Methods for Cognitive Science

  • James J. Palestro
  • Per B. Sederberg
  • Adam F. Osth
  • Trisha Van Zandt
  • Brandon M. Turner

Table of contents

  1. Front Matter
    Pages i-xiv
  2. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 1-11
  3. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 13-53
  4. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 55-79
  5. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 81-93
  6. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 95-114
  7. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 115-117
  8. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 119-119
  9. Back Matter
    Pages 121-129

About this book

Introduction

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. 


Keywords

Likelihood-free Bayesian analysis Approximate Bayesian computation Minerva 2 Tutorial Model Estimation Probability Density Approximation ABCDE Gibbs ABC Cognitive Psychology Modeling Bayesian methods

Authors and affiliations

  • James J. Palestro
    • 1
  • Per B. Sederberg
    • 2
  • Adam F. Osth
    • 3
  • Trisha Van Zandt
    • 4
  • Brandon M. Turner
    • 5
  1. 1.Department of PsychologyThe Ohio State UniversityColumbusUSA
  2. 2.Department of PsychologyThe Ohio State UniversityColumbusUSA
  3. 3.University of MelbourneParkvilleAustralia
  4. 4.Department of PsychologyThe Ohio State UniversityColumbusUSA
  5. 5.Department of PsychologyThe Ohio State UniversityColumbusUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-72425-6
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Behavioral Science and Psychology
  • Print ISBN 978-3-319-72424-9
  • Online ISBN 978-3-319-72425-6
  • Series Print ISSN 2510-1889
  • Series Online ISSN 2510-1897
  • Buy this book on publisher's site