Shared Information and Markov Tree-Based Models

Sagnik Bhattacharya, UMD

Shared information is a measure of mutual dependence among m ≥ 2 jointly distributed discrete random variables, and has been proposed as a generalization of Shannon’s mutual information. The first part of the talk will focus on some properties of shared information that make it a good measure of such mutual dependence and some applications. In the second part, I shall discuss our recent work on explicit formulae for shared information in the special case of a Markov chain on a tree and how these results help in estimating shared information when the joint distribution of the underlying random variables is not known. Joint work with Prakash Narayan.

Recorded Talk