Probabilistic graphical models

Probabilistic graphical models are very effective at modelling complex relationships among variables. These might be the relationships between symptoms and diseases, or the relationships between a set of sensor inputs and the state of the system being modelled, or the relationships between cellular metabolic reactions and the genes that encode them, or the relationships between users in a social network about whom we wish to draw inferences. 

Probabilistic graphical models use nodes to represent random variables and graphs to represent joint distributions over variables. By utilising conditional independence, a gigantic joint distribution (over potentially thousands or millions of variables) can be decomposed to local distributions over small subsets of variables, which facilitates efficient inference and learning. 

 

Projects

  • Probabilistic graphical models for interventional queries

    The project intends to develop methods to suggest how to optimally intervene so that the future state of the system will best suit our interests. The power of probabilistic graphical models to model complex relationships and interactions among a large number of variables facilitates many applications. However, such models only aim to understand the underlying environment. What is ultimately needed in many real-world applications is to suggest how we ought to intervene or act, so as to alter the environment to best suit our interests. The proposed project aims to achieve this using probabilistic graphical models on massive real-world data sets, thus facilitating a variety of applications from health care to commerce and the environment.

    A/Prof Qinfeng Shi; Assistant Professor Julian McAuley; Associate Professor Pawan Mudigonda

  • Compressive sensing based probabilistic graphical models

    Probabilistic Graphical Models (PGMs) use graphs to represent the interactions between random variables and provide a formalism by which to represent complex probabilistic relationships. Despite the success of PGMs in many fields, the learning on real industrial large scale applications is very slow. I will exploit the sparsity and compressibility in PGMs, and turn the large scale PGMs to a number of small scale PGMs. Solving these small scale PGMs and then reversely recover the solutions in the original large scale PGMs in the context of Compressive Sensing. This way, I can effectively deal with large scale PGMs in the computational complexity of small scale PGMs as well as provide theoretical guarantees on the consistency of the solution.

    A/Prof Qinfeng Shi