Locally Bayesian Learning

  title={Locally Bayesian Learning},
  author={John K. Kruschke},
This article is concerned with trial-by-trial, online lear ning of cue-outcome mappings. In models structured as successions of component functions, an external target can be back propagated such that the lower layer’s target is the input to the higher layer that maximizes the probability of the highe r layer’s target. Each layer then does locally Bayesian learn ing. The resulting parameter updating is not globally Bayesian, but can better capture human behavior. The approach is implemented for… CONTINUE READING


Publications referenced by this paper.

Similar Papers

Loading similar papers…