Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Empirical Study of MDL Model Selection with Infinite Parametric
  Complexity   Parametric complexity is a central concept in 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. The Bayesian model with the improper
Jeffreys' prior is the most dependable.

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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph