TITLE:
Minimum Description Length Methods in Bayesian Model Selection: Some Applications
AUTHORS:
Mohan Delampady
KEYWORDS:
Bayesian Analysis; Model Selection; Minimum Description Length; Hierarchical Bayes; Bayesian Computations
JOURNAL NAME:
Open Journal of Statistics,
Vol.3 No.2,
April
26,
2013
ABSTRACT: 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.