**Structure Learning for Gaussian Graphical Models **

*By **Benjamin Marlin*

Estimating a single multivariate Gaussian distribution is a challenging
problem when the number of data cases N is less than (or even close to) the
number of data dimensions D. This is due to the fact that the standard
maximum likelihood estimate for the covariance matrix is invalid when N<D,
and is highly sensitive when N>D but D/N is close to one. One solution to
dealing with this problem is to regularize the Gaussian distribution by
requiring the inverse covariance matrix to be sparse. This corresponds to
learning a Gaussian Graphical Model (GGM) with sparse connectivity
structure. In this talk I will give an overview of our recent work on
structure learning for GGMs including our work on accelerating stochastic
local search for GGMs (to appear at NIPS'09), and our work on learning
sparse GGMs with latent block structure (ICML'09 and UAI'09).

This is joint work with Kevin Murphy, Mark Schmidt, and Emtiyaz Khan at UBC and Baback Moghaddam at NASA/JPL.

This is joint work with Kevin Murphy, Mark Schmidt, and Emtiyaz Khan at UBC and Baback Moghaddam at NASA/JPL.