|Instructor:||David Pollard (firstname.lastname@example.org)|
|When:||Tuesday, Thursday 2:30 - 3:45|
|Office hours:||Wednesday 1:00 - 2:20 (more if needed)|
|TA:||Cong Huang (email@example.com)|
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 key measure theoretic ideas, with other ideas explained 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. All members of a team will be expected to understand the team's solutions sufficiently well to explain the reasoning at the blackboard. Occasional meetings with DP will be arranged.
Homework due each Thursday
Handouts, including some extracts from UGMTP and rewrites of UGMTP.
Class materials for an introductory probability course (Stat 241/541, Fall 2000), containing more extensive elementary discussion of probabilistic ideas. See, in particular, the Chapters 2 and 4, on conditional expectations and on symmetry.