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.