Tssp-2022-23

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General course info

Grading

Fall grade = 0.3 HAs + 0.7 October Exam

Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam

Actual grades: xlsx, html

Teachers and assistants

Lecturer: Peter Lukianchenko

Class teacher: Boris Demeshev, Sveta Popova, Maria Kirillova

Home assignments

Log-book

Week 1. 2022-09-03

Lecture. Markov chains, transition matrix, pdf

Class. Transition matrix, first step analysis.

More:

Cambridge course on Markov chains

Week 2. 2022-09-10

Lecture. Markov chains, stationary distribution, modes of convergence, pdf

Class. Stationary distribution, modes of convergence

Week 3. 2022-09-17

Lecture. Markov process, math modelling, pdf

Class. plim, almost sure lim

Week 4. 2022-09-24

Lecture. Conditional expectation, pdf

Class. Conditional expectation, sigma algebra, 4a, 4b

Week 5. 2022-10-01

Lecture. First-step analysis, sigma algebra pdf

Class. Conditional expectation and variance, sigma algebra, 5a, 5b

Week 6. 2022-10-08

Lecture. Basics of stochastic processes pdf

Class: Martingales, filtration, 6a, 6b

Week 7. 2022-10-15

Lecture. Brownian motion (Wiener process), filtration in continuous time pdf

Class: Poisson process, 7a, 7b

Week 8. 2022-10-22

Lecture. Wiener process (additional exercises) video, pdf

Class: Solve midterm tasks

Week 9. 2022-11-05

Lecture. Stochastic integral, Ito formula pdf

Sources

MC + MCMC

  • James Norris, Markov chains (1998, no kernels)

Stochastic Calculus

  • Zastawniak, Basic Stochastic Processes

Time Series

UCM