Time Series and Stochastic Processes ada 21 22

General course info

• Boring official web page

• tg-channel

• teams group: all class videos are there :)

• All home Assignments

Teachers and assistants

Lecturer: Peter Lukianchenko

Class teacher: Boris Demeshev

Week progress

Week 01

Lecture:

Class: First step analysis, expected time to get HTH.

Week 02

Lecture:

Class: Markov chain states classification

Week 03

Lecture:

Class: Poisson process.

Week 04

Lecture:

Class:

Week 05

Lecture:

Class: Conditional expected value. Conditional variance.

Week 06

Lecture:

Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra.

Week 07

Lecture:

Class: Probability limit, Moment generating function

Sources

• 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

• (no kernels)

• General State Space Markov Chains by Roberts and Rosenthal (+++, статья)

• 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

• Черновик задачника (рус)

UCM

• aa

• bb

• Harvey, Forecasting with UCM

• Chad Fulton

• Rob Hyndman, State Space Models