Time Series and Stochastic Processes ada 21 22 — различия между версиями
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==== Week 01 ==== | ==== Week 01 ==== | ||
| − | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_01_TSSP.pdf] |
Class: First step analysis, expected time to get HTH. | Class: First step analysis, expected time to get HTH. | ||
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==== Week 02 ==== | ==== Week 02 ==== | ||
| − | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_02_TSSP.pdf] |
Class: Markov chain states classification | Class: Markov chain states classification | ||
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==== Week 03 ==== | ==== Week 03 ==== | ||
| − | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_03_TSSP.pdf] |
Class: Poisson process. | Class: Poisson process. | ||
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==== Week 04 ==== | ==== Week 04 ==== | ||
| − | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_04_TSSP.pdf] |
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Class: Conditional expected value. Conditional variance. | Class: Conditional expected value. Conditional variance. | ||
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==== Week 05 ==== | ==== Week 05 ==== | ||
| − | Lecture: | + | Lecture: [https://github.com/bdemeshev/tssp_2021-22/raw/main/lectures/Lect_05_TSSP.pdf] |
Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra. | Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra. | ||
Версия 20:31, 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: [1]
Class: First step analysis, expected time to get HTH.
Week 02
Lecture: [2]
Class: Markov chain states classification
Week 03
Lecture: [3]
Class: Poisson process.
Week 04
Lecture: [4]
Class: Conditional expected value. Conditional variance.
Week 05
Lecture: [5]
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
