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 Networks and Other PGM software


Bayesian resources

A tutorial on DBNs

Bayesian Net ToolBox

An Intro. to Bayesian Networks

More Intro. to Bayesian Networks

How to use Dynamic Bayesian Networks

More BN resources

Judea Pearl Homepage

Cristopher M. Bishop's tutorial on graphical models

Additional links