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

Likelihood-Free Methods for Cognitive Science

Palestro, James J. author.


Personal Author
Palestro, James J. author.

Physical Description
XIV, 129 p. 27 illus., 7 illus. in color. online resource.

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. .

Subject Term
Cognitive Psychology.

Added Author
Sederberg, Per B.
Osth, Adam F.
Van Zandt, Trisha.
Turner, Brandon M.

Added Corporate Author
SpringerLink (Online service)

Electronic Access

LibraryMaterial TypeItem BarcodeCall NumberShelf LocationStatus
University LibraryeBookER211531BF201Electronic ResourcesE-resources