Performing incremental Bayesian inference by dynamic model counting

Speaker: Wei Li

The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work demonstrates that the well-known Davis-Putnam procedure combined with a dynamic decomposition and caching technique is an effective method for exact inference in Bayesian networks with high density and width. We define dynamic model counting and extend the dynamic decomposition and caching technique to multiple runs on a series of problems with similar structure. This allows us to perform Bayesian inference incrementally as the structure of the network changes. In this talk, I'll describe dynamic model counting, demonstrate its effectiveness in several experiments and compare it with static model counting method.

Joint work with Peter van Beek and Pascal Poupart.