Monte Carlo Sampling and Algebra
Ian H. Dinwoodie
Mathematics and Statistics, Portland State University, Portland, OR, United States

This talk will review algebraic methods for designing sampling algorithms. Examples will include Monte Carlo methods for multinomial and binary data with both linear and nonlinear constraints. Applications will be made to categorical data analysis including sparse tables.

Keywords: Contingency table; Algebra; Ideal; Groebner basis

Biography: Mr. Dinwoodie received the Ph.D. in 1990 at Northwestern University with Professor Sandy Zabell. His research has been on large deviations, Markov chain Monte Carlo methods, and sequential importance sampling.