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Nicholas Polson is a Bayesian statistician who conducts research on Financial Econometrics, Markov chain Monte Carlo, Particle learning, and Bayesian inference. Inspired by an interest in probability, Polson has developed a number of new algorithms and applied them to the fields of statistics and financial econometrics, including the Bayesian analysis of stochastic volatility models and sequential particle learning for statistical inference.

Polson’s article, “Bayesian Analysis of Stochastic Volatility Models,” was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics. His recent work includes methods for sparse Bayesian estimation with application to high dimensional regression and classification.

Academic Areas

  • Econometrics and Statistics

Selected Publications

Working Papers

2020 - 2021 Course Schedule

Number Course Title Quarter
41916 Bayes, AI and Deep Learning 2020 (Autumn)
41000 Business Statistics 2020 (Autumn)