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Minimum Description Length Methods in Bayesian Model Selection: Some Applications

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DOI: 10.4236/ojs.2013.32012    3,428 Downloads   5,137 Views  
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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.


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