A New Model Selection Criterion for Monte Carlo Sampling Algorithms

We present a new model selection criterion that can be easily approximated by a Monte Carlo sampling algorithm. It is shown that under certain conditions the new criterion is related to both the deviance and the Akaike information criteria. The new criterion can be easily extended to an arbitrary non-negative objective function using the extended Baum-Welch procedure.

By: Avishy Carmi; Dimitri Kanevsky

Published in: RC24670 in 2008


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