DMCA
An empirical study of minimum description length model selection with infinite parametric complexity (2006)
| Venue: | JOURNAL OF MATHEMATICAL PSYCHOLOGY |
| Citations: | 12 - 1 self |
BibTeX
@ARTICLE{Rooij06anempirical,
author = {Steven de Rooij and Peter Grünwald},
title = {An empirical study of minimum description length model selection with infinite parametric complexity},
journal = {JOURNAL OF MATHEMATICAL PSYCHOLOGY},
year = {2006},
volume = {50},
pages = {180--192}
}
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Abstract
Parametric complexity is a central concept in Minimum Description Length (MDL) model selection. In practice it often turns out to be infinite, even for quite simple models such as the Poisson and Geometric families. In such cases, MDL model selection as based on NML and Bayesian inference based on Jeffreys ’ prior can not be used. Several ways to resolve this problem have been proposed. We conduct experiments to compare and evaluate their behaviour on small sample sizes. We find interestingly poor behaviour for the plug-in predictive code; a restricted NML model performs quite well but it is questionable if the results validate its theoretical motivation. A Bayesian marginal distribution with Jeffreys’ prior can still be used if one sacrifices the first observation to make a proper posterior; this approach turns out to be most dependable.
Keyphrases
infinite parametric complexity minimum description length model selection empirical study central concept small sample size poor behaviour simple model parametric complexity nml model performs bayesian marginal distribution proper posterior bayesian inference minimum description length several way mdl model selection theoretical motivation first observation plug-in predictive code geometric family model selection
