grant

Compressive sensing based probabilistic graphical models (PGM) [ 2012-01-02 - 2017-06-30 ]

Research Grant

[Cite as http://purl.org/au-research/grants/arc/DE120101161]

Researchers: Dr Qinfeng Shi (Discovery Early Career Researcher Award)

Brief description Compressive sensing based probabilistic graphical models (PGM). The aim of the project is to develop fast, large scale probabilistic graphical models (PGM) learning and inference methods. The resulting system will be able to process large scale PGMs on a standard PC, and will be easily extendable to computer clustering for larger scale PGMs requiring higher precision.

Funding Amount $375,000

Funding Scheme Discovery Early Career Researcher Award

View this grant in the ARC Data Portal

Click to explore relationships graph
Identifiers
Viewed: [[ro.stat.viewed]]