Temporally Invariant Junction Tree for Inference in Dynamic Bayesian Network

Dynamic Bayesian networks (DBNs) extend Bayesian networks from static domains to dynamic domains. The only known generic method for exact inference in DBNs is based on dynamic expansion and reduction of active slices. It is effective when the domain evolves relatively slowly, but is reported to be “too expensive” for fast evolving domain where inference is under time pressure. This study explores the stationary feature of problem domains to improve the efficiency of exact inference in DBNs. We propose the construction of a temporally invariant template of a DBN directly supporting exact inference and discuss issues in the construction. This method eliminates the need for the computation associated with dynamic expansion and reduction of the existing method. The method is demonstrated by experimental result.