Model averaging, optimal inference, and habit formation.

ABSTRACT: Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be trade

SUBMITTER: FitzGerald TH 

PROVIDER: S-EPMC4071291 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature