Instructor: | David Pollard |
Email: | david.pollard@yale.edu |
When: | Tuesday, Thursday 2:30 - 3:45 |
Where: | 24 Hillhouse Avenue (Statistics Department) |
Office hours: | Wednesday 1:00-3:00; and after lectures on Tuesday and Thursday (more hours to be arranged if necessary) |
Measure theoretic probability, conditioning, laws of large numbers,
convergence in distribution, characteristic functions, central limit
theorems, martingales. Some knowledge of real analysis is assumed.
Knowledge of measure theory is not assumed. The first two weeks of the course will introduce measure theoretic ideas (sigma-fields, countable additivity, integrals, monotone convergence, dominated convergence, generating classes--compare with the chapter Modicum.pdf from spring 99). Other measure theoretic ideas will be introduced during the course, as needed.
If you prefer a more standard text, one of the books on the list of references might be to your taste.
Students who wish to work in teams (no more than 3 to a team) should submit a single a solution set, which will be discussed during a weekly meeting with DP. All members of a team will be expected to understand the team's solutions sufficiently well to explain the reasoning at the blackboard.
Current versions of notes
Class materials from spring 99 (with additions).
EMAIL: questions and answers regarding problem sets, notes, and anything else related to the course.
Class materials for an introductory probability course (Stat 241/541, Fall 97), containing more extensive elementary discussion of probabilistic ideas. See, in particular, the Chapters 2 and 5, on conditional expectations and on symmetry.
DBP 16 Jan
2000