Probability II: Discrete time processes
Summary
A first course in advanced theoretical probability, focused on discrete families of random variables.
Content
Topics to be covered:
- Random variables and convergence
- Sums of i.i.d. random variables
- Conditional Expectation and probability
- Markov Chains (discrete time)
- Martingales (discrete time)
Time permitting, we will study applications of the previous topics to
- Branching Processes
- Percolation
Learning Prerequisites
Required courses
Analysis I to IV, Linear algebra, Probability
Recommended courses
Some familiarity with measure theory and general topology is helpful but not necessary.
Assessment methods
Written
Resources
Notes/Handbook
Lectures notes will be available on moodle.
Moodle Link
In the programs
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability II: Discrete time processes
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability II: Discrete time processes
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability II: Discrete time processes
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability II: Discrete time processes
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional