Time Series and Stochastic Processes ada 21 22 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Bdemeshev (обсуждение | вклад) (→Time Series) |
Bdemeshev (обсуждение | вклад) (→MC + MCMC) |
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* [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains | * [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains | ||
| − | * [https://eml.berkeley.edu/reprints/misc/understanding.pdf | + | * Chib and Greenberg, [https://eml.berkeley.edu/reprints/misc/understanding.pdf Understanding MH algorithm] |
| − | * [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf | + | * Casella, [http://biostat.jhsph.edu/~mmccall/articles/casella_1992.pdf Explaining Gibbs Sampler] |
| − | * | + | * Roberts and Rosenthal, [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains] |
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* [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods] | * [https://chi-feng.github.io/mcmc-demo Visualization of MCMC methods] | ||
| − | * [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf | + | * Charles Geyer, [http://www.stat.umn.edu/geyer/f05/8931/n1998.pdf MCMC lecture notes] (with a little bit of kernels!) |
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=== Stochastic Calculus === | === Stochastic Calculus === | ||
Версия 20:02, 27 октября 2021
Содержание
General course info
- Boring official web page
- teams group: all class videos are there :)
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: Conditional expected value. Conditional variance.
Week 05
Lecture:
Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra.
Week 06
Lecture:
Class: Probability limit, Moment generating function
Midterm
The long-awaited midterm will be on 28 October, 10:00 - 12:00.
Duration: 120 minutes. No proctoring.
Topics:
- First step analysis
- Classification of states and classes of MC.
- Conditional expected value (two views).
- Poisson process.
- Sigma algebras.
- Probability limit
- Moment generating function
Sources
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
- Charles Geyer, MCMC lecture notes (with a little bit of kernels!)
Stochastic Calculus
- Zastawniak, Basic Stochastic Processes
Time Series
- Van der Vaart, Time Series
UCM
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter
