Sequential Model Criticism in Probabilistic Expert Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Mixture reduction via predictive scores
Statistics and Computing
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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Given a Bayesian network of discrete random variables with a hyper-Dirichlet prior, a method is proposed for assigning Dirichlet priors to the conditional probabilities of structurally different networks. It defines a distance measure between priors which is to be minimized for the assignment process. Intuitively one would expect that if two models' priors are to qualify as being 'close' in some sense, then their posteriors should also be nearby after an observation. However one does not know in advance what will be observed next. Thus we are led to propose an expectation of Kullback-Leibler distances over all possible next observations to define a measure of distance between priors. In conjunction with the additional assumptions of global and local independence of the parameters [15], a number of theorems emerge which are usually taken as reasonable assumptions in the Bayesian network literature. The method is compared to the 'expansion and contraction' algorithm of [14], and is also contrasted with the results obtained in [7] who employ the additional assumption of likelihood equivalence which is not made here. A simple example illustrates the technique.