Mark Huber, Ph.D.
Department
Areas of Expertise
Biography
I work in the area of computational probability, designing Monte Carlo methods for applications in statistics and computer science.
Teaching Interests
Statistics, Numerical Methods, Probability
Research Interests
Primary emphasis is on the design and analysis of new perfect sampling methods that draw variates exactly from high-dimensional target distributions.,Monte Carlo simulation and stochastic computation for statistical applications, approximation algorithms, and numerical integration.
Education
B.S., Harvey Mudd College; Ph.D., Cornell University.
Awards and Affiliations
NSF CAREER award
NSF Postdoctoral Fellow in Mathematical Sciences NSF CAREER award
Research and Publications
M. Huber. Spatial point processes. In S. Brooks, A. Gelman, G. Jones, and X. Meng, editors, Handbook of MCMC, pages 227–252. Chapman & Hall/CRC Press, 2011.
M. Huber and J. Law, Fast approximation of the permanent for very dense problems, Proc. of Symposium on Discrete Algorithms (2008), pp. 681–689.
M. L. Huber, Fast perfect sampling from linear extensions, Discrete Mathematics, vol. 306 (2006), pp. 420–428.
M. Huber, Y. Chen, I. Dinwoodie, A. Dobra, and M. Nicholas, Monte Carlo algorithms for Hardy-Weinberg proportions, Biometrics, vol. 62 no. 1 (March, 2006), pp. 49–53.
M. Huber, Perfect sampling using bounding chains, The Annals of Applied Probability, vol. 14 no. 2 (August, 2004), pp. 734–753.