Monte Carlo Methods

Monte Carlo methods reside at the interface of numerical methods and statistics. In this course students will be introduced to both stochastic and deterministic methods for approximating integrals numerically. We will discuss how both algorithmic and statistics approaches may be applied to improve these methods, with a focus on sample reuse, control variates, importance sampling, and stratified sampling. We will also explore connections to problems in other fields such as radiography, rendering, and nuclear fusion.



Michael Czekanski is a statistician working on improved Monte Carlo methods through high performance computing for research in rendering, nuclear fusion, and other areas.