The term "Monte Carlo" refers to a broad class of numerical algorithms which rely on repeated random sampling. Since its beginnings in the late 1940s at the Los Alamos National Laboratory, Monte Carlo has continued to gain in importance in scientific use. The continued growth of computing power coupled with a drastic decrease in price in the last decade, means that Monte Carlo methods are now more practical than ever.
In this course, we will discuss what Monte Carlo methods are, and we will look at their varied applications. The three main topics we will cover are (a) "basic" Monte Carlo integration, (b) Markov chain Monte Carlo, and (c) bootstrap methodology. Applications will be taken from various sciences, including statistics, operations research, and actuarial science.
The hope is that a significant portion of the course will be spent in the computer lab, using the statistical software R to perform Monte Carlo simulations. Previous experience with computing will be an asset, but is not required.
Instructor: Hanna Jankowski
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Office: N621B Ross
Office hours: Tuesdays, 1-2pm. No drop-ins, please.