Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Prerequisites
18.440 Probability and Random Variables or 6.041SC Probabilistic Systems Analysis and Applied Probability
Description
This course is an introduction to Markov chains, random walks, martingales, and Galton-Watsom tree. The course requires basic knowledge in probability theory and linear algebra including conditional expectation and matrix.
Recommended Textbooks
Levin, David Asher, Y. Peres, and Elizabeth L. Wilmer. Markov Chains and Mixing Times. American Mathematical Society, 2008. ISBN: 9780821847398. [Preview with Google Books]
Williams, D. Probability with Martingales. Cambridge University Press, 1991. ISBN: 9780387985091.
Brémaud, Pierre. Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues. Springer, 2008. ISBN: 9780387985091. [Preview with Google Books]
Assignments and Exams
There are 5 homework assignments, 1 midterm exam, and final exam. The midterm and the final exams are closed book, closed notes, and no calculators.
Grading
| ACTIVITIES | PERCENTAGES |
|---|---|
| Assignments | 50% (10% each) |
| Midterm Exam | 15% |
| Final Project | 35% |