Uncertainty in Artificial Intelligence
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Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Peter Hooper
Abstract:
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each node, a table of parameters (CPtable) represents local conditional probabilities, with rows indexed by conditioning events (assignments to parents). CP-table rows are usually modeled as independent random vectors, each assigned a Dirichlet prior distribution. The assumption that rows are independent permits a relatively simple analysis but may not reflect actual prior opinion about the parameters. Rows representing similar conditioning events often have similar conditional probabilities. This paper introduces a more flexible family of "dependent Dirichlet" prior distributions, where rows are not necessarily independent. Simple methods are developed to approximate the Bayes estimators of CP-table parameters with optimal linear estimators; i.e., linear combinations of sample proportions and prior means. This approach yields more efficient estimators by sharing information among rows. Improvements in efficiency can be substantial when a CP-table has many rows and samples sizes are small.
Keywords:
Pages: 251-259
PS Link:
PDF Link: /papers/04/p251-hooper.pdf
BibTex:
@INPROCEEDINGS{Hooper04,
AUTHOR = "Peter Hooper ",
TITLE = "Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "251--259"
}


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