Open AccessMinimum Description Length Methods in Bayesian Model Selection: Some Applications
Author(s) -
Mohan Delampady
Publication year - 2013
Publication title -
open journal of statistics
Language(s) - English
Resource type - Journals
eISSN - 2161-7198
pISSN - 2161-718X
DOI - 10.4236/ojs.2013.32012
Subject(s) - minimum description length , bayesian probability , computer science , model selection , bayesian information criterion , bayesian experimental design , selection (genetic algorithm) , variable order bayesian network , algorithm , bayesian inference , computation , artificial intelligence , machine learning , mathematics
Computations involved in Bayesian approach to practical model selection problems are usually very difficult. Computational simplifications are sometimes possible, but are not generally applicable. There is a large literature available on a methodology based on information theory called Minimum Description Length (MDL). It is described here how many of these techniques are either directly Bayesian in nature, or are very good objective approximations to Bayesian solutions. First, connections between the Bayesian approach and MDL are theoretically explored; thereafter a few illustrations are provided to describe how MDL can give useful computational simplifications.