For probabilistic graphical models, we have significant experience in developing theories for graphical model learning and efficient inference for different types of graphical models, including directed , undirected, and hybrid models. For graphical model learning, we have developed algorithms for learning both the model parameters and structure, and learning the model under incomplete training data with qualitative constraints. For inference, we focus on developing methods that can perform efficient and active inference. We also developed a unified probabilistic framework based on combining the directed and undirected graphs to produce a more expressive framework that can capture heterogeneous knowledge at different levels of abstraction.
Local Structure Learning and Causal Discovery
Deep Regression Bayesian Networks
We have applied graphical models to different applications including human activity modeling and recognition, facial activity modeling and understanding, image and video segmentation, military planning, and information fusion for decision making and situation awareness. PGM Application projects include:
(1) Human Activity Modeling and Recognition (Sponsored by DARPA, Army)
(2) Facial Activity Modeling and Understanding (Sponsored by DARPA and ONR)
(3) Dynamic and Active Information Fusion for Decision Making Under Uncertainty (Sponsored by AFOSR and ARO)
(4) Efficient Effects-based Military Planning (sponsored by ARO)
The software for Bayesian network structure learning can be downloaded here.
Some useful software packages for graphical models can be found here.
A good R package for BN structure learning can be found here.
A library of causal discovery algorithms based on Bayesian Network learning theory: CausalExplorer.
Another package for graphical models in R: CRAN Task.
Most recent softwares for BN structure learning: GOBNILP, URLearning, best-w-tree, TWILP, SparsityBoost, OpenGM.
Some useful online resources for PGM. [link]
The latest list of related papers can be found here.