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 the 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.
CITATION STYLE
Kontkanen, P., Myllymiki, P., Silander, T., Tirri, H., & Grünwald, P. (1998). Bayesian and information-theoretic priors for bayesian network parameters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1398, pp. 89–94). Springer Verlag. https://doi.org/10.1007/bfb0026676
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