Mark Huber Publications

Lattice Points, contingency tables, and sampling
Y. Chen, I. Dinwoodie, A. Dobra, and M. Huber, Contemporary Mathematics, vol. 374 (2005), pp. 6578.

Abstract: Markov chains and sequential importance sampling (SIS) are described as two leading sampling methods for Monte Carlo computations in exact conditional inference on discrete data in contingency tables. Examples are explained from genotype data analysis, graphical models, and logistic regression. A new Markov chain and implementation of SIS are described for logistic regression.


This site supported by NSF CAREER grant DMS-05-48153. Last update: 04 December 2009. Note: All downloads provided solely for use within the restrictions of the Fair Use Act, and all copyrights remain with their respective owners.