Likelihood-free Bayesian analysis of memory models.

ABSTRACT: Many influential memory models are computational in the sense that their predictions are derived through simulation. This means that it is difficult or impossible to write down a probability distribution or likelihood that characterizes the random behavior of the data as a function of the model's parameters. In turn, the lack of a likelihood means that these models cannot be directly fitted to data using traditional techniques. In particular, standard Bayesian analyses of such models are impossible. In this article, we examine how a new procedure called approximate Bayesian computation (ABC), a method for Bayesian analysis that circumvents the evaluation of the likelihood, can be used to fit computational models to memory data. In particular, we investigate the bind cue decide model of epi

SUBMITTER: Turner BM 

PROVIDER: S-EPMC4140406 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature