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.
