Discrete Bayesian Networks: The Exact Posterior Marginal Distributions
This work provides a method for efficient exact inference in Bayesian networks, which is incremental as it builds on existing algorithms like the revised polytree algorithm.
The paper tackles the problem of computing exact marginal probabilities for query variables in Bayesian networks by introducing the border algorithm to convert networks into directed chains and a parentless polytree method to transform any network into a polytree, achieving inference complexity linear in the number of evidence and query variables.
In a Bayesian network, we wish to evaluate the marginal probability of a query variable, which may be conditioned on the observed values of some evidence variables. Here we first present our "border algorithm," which converts a BN into a directed chain. For the polytrees, we then present in details, with some modifications and within the border algorithm framework, the "revised polytree algorithm" by Peot & Shachter (1991). Finally, we present our "parentless polytree method," which, coupled with the border algorithm, converts any Bayesian network into a polytree, rendering the complexity of our inferences independent of the size of network, and linear with the number of its evidence and query variables. All quantities in this paper have probabilistic interpretations.