Why AI Models Work When Theory Suggests They Shouldn鈥檛
Using a Bayesian framework helps explain ‘double descent.’
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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鈥檚 article, 鈥淏ayesian Analysis of Stochastic Volatility Models,鈥� 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,"听Handbook of Financial Econonmetrics听(2004).
With B. Eraker and M. Johannes, "The Impact of Jumps in Volatility in Returns,"听Journal of Finance听(2003).
With E. Jacquier and P. Rossi, "Bayesian Analysis of Stochastic Volatility Models,"听Journal of Business and Economic Statistics听(1994, 2002).
Invited paper with discussion, "Convergence of Markov Chain Monte Carlo Algorithms,"听Fifth Valencia Meeting on Bayesian Statistics.
For a listing of research publications, please visit the听.
Working Papers
2025 - 2026 Course Schedule
| Number | Course Title | Quarter |
|---|---|---|
| Bayes, AI and Deep Learning | 2025 (Autumn) | |
| Business Statistics | 2025 (Autumn) |
Get Insights from Nicholas Polson in 黑料传送门 Booth Review
What Are the Odds of Cheating?
Research highlights how unsound statistical analysis can lead to flawed conclusions.
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Did a US Chess Champion Cheat?
Statistical analysis suggests both an answer and a note of caution about data framing.
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