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NeuroCOLT
Technical Report NC-TR-98-002
A
comparison of non-informative priors for Bayesian networks
Peter
Grunwald
CWI, Amsterdam
Keywords:
Stochastic complexity; Bayesian networks; MML
Received:
06-MAR-1998
Abstract
We consider Bayesian and information-theoretic approaches for determining
non-informative prior distributions in a parametric model family.
The information-theoretic approaches are based on the recently modified
definition of stochastic complexity by Rissanen, and on the Minimum
Message Length (MML) approach by Wallace. The Bayesian alternatives
include the uniform prior, and various equivalent sample size priors.
In order to be able to empirically compare the different approaches
in practice, the methods are instantiated for a model family of practical
importance, the family of Bayesian networks. The results with several
public domain datasets show that the choice of the prior distribution
can have a significant effect on the results obtained, especially
if the amount of the data available is small. Inspired by our empirical
observations, we also introduce a new heuristics for determining the
prior distribution. The empirical results show that the heuristics
gives consistently very good results with respect to the results obtained
by alternative methods.
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