Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.

ABSTRACT: Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Ba

SUBMITTER: Piray P 

PROVIDER: S-EPMC6581260 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature