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Likelihood-Free Methods for Cognitive Science / / James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.

By: Palestro, James J., autor
Contributor(s): Sederberg, Per B., autor | Osth, Adam F., (1985-), autor | Van Zandt, Trisha, autor | Turner, Brandon M., (1985-), autor | SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: (Computational Approaches to Cognition and Perception, 2510-1889 ); (Behavioral Science and Psychology (Springer-41168)).Publisher: Cham : Springer International Publishing, 2018Edition: First edition 2018.Description: 1 recurso en línea (XIV, 129 páginas) : 27 ilustraciones, 7 ilustraciones a color.ISBN: 9783319724256.Subject: Ciencia cognitiva -- MetodologíaOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Chapter 1. Motivation -- Chapter 2. Likelihood-Free Algorithms -- Chapter 3. A Tutorial -- Chapter 4. Validations -- Chapter 5. Applications -- Chapter 6. Conclusions -- Chapter 7. Distributions.
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. .
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias Sociales BF311 2018 EB (Browse shelf(Opens below)) Acceso electrónico eBook.14052110
Total holds: 0

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. .

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