Course Description
This Ph.D. level course is intended to give students a broad sense of the different mathematical and computational tools and models available to analyze systems in which uncertainty is present. The key ideas underlying stochastic analysis will be presented in a mathematically careful way, and illustrated using various applications chosen from engineering, the physical sciences, and economics. This course is intended both to introduce students to the subject matter at an advanced level and to offer an entry point into the many other high-level stochastics courses that Stanford offers.
Contact Information
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Please use the following list to contact the CA's with questions regarding homework: cme308-2008-cas@lists.stanford.edu. This way, you will get a quicker reply. Also, it will help us judge the pace that everyone is going at, and not just the people who send us individual emails.
Lectures
| Time: | Tuesdays and Thursdays 11:00 to 12:15 PM |
| Location: | Gates, B3 |
Prerequisites
Knowledge of sample space, events, probability, conditional probability, independence, random variables, jointly distributed rvs, probability mass functions, probability density functions, expectations, the law of large numbers, central limit theorem.
Suggested Reference: Introduction to Probability Models by Sheldon M. Ross (Academic Press), Chapters 1 to 3
See also: Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues by Pierre Bremaud (Springer), Chapter 1, Sections 1 to 7
Familiarity with linear algebra, basic real variables and analysis, and differential equations is also useful.
Textbook
There is no required textbook for this class. We will shortly post a concise listing of useful references.
Homework
There will be assignments due roughly every two weeks. Collaboration among students is encouraged. You should feel free to discuss problems with your fellow students (please document on each assignment with whom you worked). However, you must write your own solutions, and copying homework from another student (past or present) is forbidden. The Stanford Honor Code will apply to all assignments, both in and out of class.
As LaTeXed solutions are easier to grade (and use parts of for solutions) there will be some incentive for LaTexing assignments. The specific policy will be posted shortly.
Late assignments will not be accepted without an extension from Prof. Glynn.
Exams
There will be no midterm. The final exam is scheduled for Tuesday, June 10, from 7 PM to 10 PM. It will be held in Gates B3, the same room as the lectures. It is open notes and open book.
Grading
The course grade will be based on assignments (50%) and the final exam (50%).