Probabilistic Reasoning using Graphical
Models
In probabilistic
graphical models, we have significant experience in developing theories for
graphical model learning and efficient inference for different graphical models
including Hidden Markov Models, Conditional Random Fields, Dynamic Bayesian
Networks, and the Influence Diagrams. In graphical model learning, we have
developed algorithms for learning 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
timely inference by factoring out the common computations and by identifying
the most informative evidences to use.
We are also developing 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.
Related Work:
2) A hybrid
graphical model based on the extended chain graph
3) Software
In
addition, 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.
Current
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)