STA 447/2006: Stochastic Processes

Wenlong Mou, Department of Statistical Sciences, University of Toronto, Winter 2026

Schedule

lecture topic book chapteres notes
01/07 introduction, Markov definitions and transition probabilities, recurrent and transient states, recurrent state theorem, recurrence of (multi-dimensional) random walks Rosenthal 1.1 – 1.5 Lecture 1
01/14 Proof of recurrence state theorem, f-expansion, relation between recurrence and transience of different states, equivalent characterization of recurrent/transient Markov chains Rosenthal 1.6, 1.7 Lecture 2
01/21 Recurrence equivalence continued, reducible Markov chains, stationary distributions and stationary measures, detailed balance condition, vanishing transition probabilities and non-existence results, periodicity Rosenthal 1.6, 2.1 – 2.3, Lawler 1.3 Lecture 3
01/28 Markov chain convergence theorem, coupling, average convergence for periodic chains, positive recurrence and null recurrence, strong law of large numbers for Markov chains Rosenthal 2.4, 2.5, 2.8, Durrett 1.7 Lecture 4
02/04 Midterm exam #1
02/11 Metropolis–Hastings algorithm, concepts of martingales and stopping times, optional stopping theorem, application to gambler's ruin Rosenthal 2.6 , Rosenthal 3.1, 3.2, Lawler 5.3 Lecture 5
02/25
03/04
03/11 Midterm exam #2
03/18
03/25
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 1.70, 1.72

  • Week 4: Rosenthal 2.4.18, 2.4.20, Lawler 1.15, Durrett 1.73, 1.74, 1.75, (see also a book, Chapter 4 and 5 for more information about coupling methods)

  • Week 5: (midterm exam 1)

  • Week 6: Rosenthal 3.1.7, 3.2.3, 3.2.8, 3.2.9, 3.2.10, 3.2.11, Lawler 5.4, 5.8, 5.9 (correction)