Tssp-2022-23

General course info

• Boring official web page

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

Teachers and assistants

Lecturer: Peter Lukianchenko

Class teacher: Boris Demeshev, Sveta Popova, Maria Kirillova

Log-book

Week 1. Markov chains.

Lecture 1. Definition, transition probabilities, pdf

Class 1. Transition probabilities, first step analysis.

More:

Cambridge course on Markov chains

Sources

• Wiki 2020-21, Wiki 2021-22
• Git repo 2020-21, Git repo 2021-22
• TG chat 2022-23
• видео семинаров 2022-23 на русском

• Statistics cookbook

MC + MCMC

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

• Cambridge course on Markov chains

• Chib and Greenberg, Understanding MH algorithm

• Casella, Explaining Gibbs Sampler

• Roberts and Rosenthal, General State Space Markov Chains

• Visualization of MCMC methods

• Charles Geyer, MCMC lecture notes (with a little bit of kernels!)

Stochastic Calculus

• Zastawniak, Basic Stochastic Processes

• Exams of ICEF master course

• Заметки магистерского курса МИЭФ (рус)

• Черновик учебника (рус)

• Черновик задачника (рус)

Time Series

• Forecasting principles and practice (R)

• Shumway, Stoffer Time Series Analysis

• Ruey Tsay web page

• Van der Vaart, Time Series

• Черновик задачника (рус)

UCM

• Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle

• João Tovar Jalles, Structural Time Series Models and the Kalman Filter

• Harvey, Forecasting with UCM

• Chad Fulton

• Rob Hyndman, State Space Models