Why AI Models Work When Theory Suggests They Shouldn’t
Using a Bayesian framework helps explain ‘double descent.’
{PubDate}
Biography
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
With M. Johannes, "MCMC Methods for Financial Econometrics," Handbook of Financial Econonmetrics (2004).
With B. Eraker and M. Johannes, "The Impact of Jumps in Volatility in Returns," Journal of Finance (2003).
With E. Jacquier and P. Rossi, "Bayesian Analysis of Stochastic Volatility Models," Journal of Business and Economic Statistics (1994, 2002).
Invited paper with discussion, "Convergence of Markov Chain Monte Carlo Algorithms," Fifth Valencia Meeting on Bayesian Statistics.
For a listing of research publications, please visit the university library listing page.
Working Papers
2025 - 2026 Course Schedule
Get Insights from Nicholas Polson in Chicago Booth Review
What Are the Odds of Cheating?
Research highlights how unsound statistical analysis can lead to flawed conclusions.
{PubDate}
Did a US Chess Champion Cheat?
Statistical analysis suggests both an answer and a note of caution about data framing.
{PubDate}