Title: A Divide-and-Conquer Method for Large Bayesian Networks

When: Monday, April 5, 2004 12:00 noon

Where: IS Rm 403

Who: Seongho Kim

Abstract: We propose an ML estimation method for a recursive model of categorical variables which is too large to handle as a single model. We first split the whole model into a set of submodels which can be arranged in the form of a tree. Two conditions are suggested as an instrument for estimating the parameters of the whole model yet working within individual submodels. Theorems are proved to the effect that, when missing values are involved, we can generalize and apply the principle of EM to the tree of submodels so that the ML estimation is possible for a recursive model of any size. For illustration, simulation experiments of the ML estimation are carried out for recursive models of up to 158 binary variables, and the proposed method is applied successfully to real data where 28 binary variables are involved.

Speaker Bio:
Ph.D course in Applied Mathematics (March 2000 - present)
        Korea Advanced Institute of Science and Technology, Korea
M.S in Applied Mathematics (March 1998 - February 2000)
        Korea Advanced Institute of Science and Technology, Korea
M.S course in Mathematics (March 1997 - February 1998)
        Chonnam National University, Korea
B.S in Mathematics (March 1990 - August 1996)
        Chonnam National University, Korea