Stat 362: Monte Carlo Methods

Overview

Monte Carlo methods are used in many application areas, including: finance, bioinformatics, computer graphics, discrete event simulation, physics, and statistical inference. We will cover a broad selection of topics, touching on some applications, and on recent developments in Markov chain Monte Carlo and quasi-Monte Carlo.

Here is a list of topics. Last time we were able to cover almost all of them. If time permits I will add some sequential Monte Carlo, squeezing down variable generation and variance reduction to make room. Monte Carlo methods are used in almost every branch of science and engineering. The topics that are most important are .... the ones that help you solve your problems. This varies from person to person. I've selected topics ranging from fundamental, that almost everybody needs a little, to specialized that some people will need a lot. The students have come from: statistics, computer science (graphics, machine learning, information retrieval), finance, biology, education, aero-astro, in the recent past.

Instructor

Art Owen
Sequoia Hall 130
My userid is owenpenguin on stanfordpenguin.edu (remember to remove the Antarctic birds or your email will bounce)
Office hour: Wednesday 11:00-12:00

TAs

• Hera He   yhe1penguin@stanfordpenguin.edu   Office Hours: Monday and Thursday 12-1 pm  Sequoia Hall 232
• Delete the Antarctic bird from the TA's email

The class text is one I'm writing. The first chapters are here. I will add some more in the homework site.

More references appear here. (A few links are broken at present.)

Evaluation

• Homework: about 4 problem sets
• The homework will involve a mix of programming assignments using Monte Carlo and theoretical exercises. The mix is tilted towards applying Monte Carlo. Most students solve the problems with R or MATLAB, whichever they are most fluent with. You can use python or Mathematica or C++ other tools (no spreadsheets though).

Some part of the home work will be made optional for students taking 362 for only 2 units instead of 3.

Be sure to give Axess a working email address:

I expect to send a small number of important emails about the problem sets to the class via Axess. Most other announcements will be made in class. Also make sure to put stat 362 in the subject line of emails. Otherwise your email won't come to the top when I search for course related emails and I might not see it until end of quarter.
Late penalties apply:
The assigned homework is due in class on paper on the date assigned. After class ends it becomes one day late. 24 hours after class ends it becomes two days late, etc. Each day late is penalized by 10% of the homework value. Homework more than 4 days late will ordinarily get 0. If you're travelling, you can email a pdf file. Late work can be pushed under my door. Never in my mail box.

To allow for sickness, interviews and other events, up to 3 days of late work are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)