STA 447/2006: Stochastic Processes

Wenlong Mou, Department of Statistical Sciences, University of Toronto, Winter 2025

Schedule

lecture topic book chapteres notes
01/08 introduction, Markov definitions and transition probabilities, recurrent and transient states, recurrent state theorem Rosenthal 1.1 – 1.5 Lecture 1
01/15 recurrence of multi-dimensional random walks, f-expansion, gambler's ruin, communicating states and irreducibility, recurrence equivalence theorems Rosenthal 1.5 – 1.7 Lecture 2
01/22 reducible Markov chains, stationary distribution, reversible Markov chains, criteria for non-existence of stationary distribution, periodicity Rosenthal 2.1 – 2.3, Lawler 1.5 Lecture 3
01/29 Markov chain convergence theorem, stationary measure of recurrent Markov chains, mean recurrence time and positive recurrence, strong law of large numbers for Markov chains Rosenthal 2.4, 2.5, 2.8, Durrett 1.7 Lecture 4
02/05 Midterm exam #1, additional topics: strong law of large numbers for Markov chains, applications to MCMC Durrett 1.7, Rosenthal 2.6 Lecture 5, Solutions
02/12 Martingales and stopping times, optional stopping theorems and their proofs, application to gambler's ruin problem Rosenthal 3.1, 3.2, Lawler 5.3 Lecture 6
02/26
03/05
03/12 Midterm exam #2
03/19
03/26
04/02

Recommended practice questions

  • Week 1: Rosenthal 1.3.9, 1.4.8, 1.5.4, 1.5.9

  • Week 2: Rosenthal 1.5.13, 1.5.14, 1.6.20, 1.6.21, 1.6.25, 1.7.12

  • Week 3: Rosenthal 2.1.3, 2.4.13, 2.4.14, 2.4.15, 2.4.16, Lawler 1.7, Durrett 1.72

  • Week 4: Rosenthal 2.4.18, 2.4.20, Durrett 1.70, 1.73, 1.74, 1.75, (if interested, there is a book on Markov chain convergence speeds)

  • Week 5: (midterm exam 1)

  • Week 6: Rosenthal 3.1.1, 3.2.3, 3.2.8, 3.2.9, 3.2.10, 3.2.11, Lawler 5.4, 5.8, 5.9 (correction)