Minimum Description Length Methods in Bayesian Model Selection: Some Applications ()

Mohan Delampady

Statistics and Mathematics Unit, Indian Statistical Institute, Bangalore, India.

**DOI: **10.4236/ojs.2013.32012
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Statistics and Mathematics Unit, Indian Statistical Institute, Bangalore, India.

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.

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M. Delampady, "Minimum Description Length Methods in Bayesian Model Selection: Some Applications," *Open Journal of Statistics*, Vol. 3 No. 2, 2013, pp. 103-117. doi: 10.4236/ojs.2013.32012.

Conflicts of Interest

The authors declare no conflicts of interest.

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