March 29, 2013
Computing and information sciences distinguished lecture series presents Prakash Shenoy today
Prakash Shenoy, Ronald G. Harper distinguished professor of artificial intelligence at the University of Kansas, will present "Inference in Hybrid Decision Networks Using Mixtures of Polynomials" in two parts on March 29.
Part one will start at 12:30 p.m. in Room 226 at the K-State Student Union and part two will start at 4:30 p.m. in 122 Nichols Hall.
The abstract for the lecture is as follows:
We describe a framework and an algorithm for approximately solving a class of hybrid decision networks (DNs) containing discrete and continuous chance variables, discrete and continuous decision variables, and deterministic conditional distributions for chance variables. A conditional distribution for a chance variable is said to be deterministic if its variances, for each state of its parents, are all zeroes. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. To mitigate the integration and optimization problems associated with solving hybrid IDs, we propose using mixture of polynomials approximations of conditional probability density and utility functions, and piecewise linear approximations of nonlinear deterministic conditional distributions for continuous chance variables. The class of hybrid DNs that can be solved by our framework are those that do not involve divisions. The framework and algorithm are illustrated by solving two small examples of hybrid IDs. In part I, we will cover the basics. In part II, we will go through the details of handling deterministic conditionals, finding mixture of polynomials approximations of conditional probability density functions, and finding piecewise linear approximations of nonlinear functions.