STATS 621
Probability Theory
The course is a self-contained rigorous measure-theoretic introduction to probability theory. Topics covered include measure and probability spaces, random variables, independence, expectation, convergence, laws of large numbers, convergence in distribution, central limit theorems, conditional expectation and martingales. Additional topics in modern probability theory chosen by the instructor are covered in the last few weeks of the course.
Topics
- measure and probability spaces
- random variables
- independence
- expectation
- convergence
- laws of large numbers
- convergence in distribution
- central limit theorems
- conditional expectation and martingales
Topics explicitly listed in official catalogue description
Prerequisites & course information
STATS 510 or equivalent, real analysis (MATH 451 or equivalent). (3). May be repeated for a maximum of 6 credits.
Sources
- Course catalogue ↗2026-2027