Institute of Information & Mathematical Sciences, Massey University, Albany Campus
University of Waikato, Room I1.09
This talk will give an overview of the issues involved in fitting, and then interpreting, Gaussian graphical models for high dimensional data. We will discuss both formulations based on undirected graphical models, and methods based on first fitting an acyclic directed graph. We introduce priors over the graphical structure that encourage sparsity, discuss model search strategies, and finally consider the process of extracting substantive insights from the fitted graph and precision matrix. We formulate this final step in terms of assigning path weights that represent the importance of different intermediaries between two correlated variables.
(Joint work with Carlos Carvalho, Adrian Dobra and Mike West)