In probabilistic reasoning, our research focuses on three aspects: active
inference, efficient inference, and model learning. Specifically, in active inference, we focus on
developing algorithms and techniques that can identify the most informative evidences to use in order
to perform effective inference in an efficient and timely manner. For efficient inference, our research studies
the issue of how to perform efficient belief propagation of the effects of the observed evidences.
For model learning, our current research focuses on learning the graphical models by combining
quantitative and qualitative data. We are also developing a unified probabilistic framework based
on combining the directed and undirected graphs through the factor graph model.
Details may be found here
Internet Resources on BNs
Bayesian resources
A tutorial on DBNs
Bayesian Net ToolBox
An Intro. to Bayesian Networks
Another Intro. to Bayesian Networks
More Intro. to Bayesian Networks
How to use Dynamic Bayesian Networks
More BN resources
Judea Pearl Homepage
Bayesian Network course
Additional materials on BNs
Knowledge Based Systems Group at UIUC
Cristopher M. Bishop's tutorial on graphical models