Tssp-2023-24 — различия между версиями
Bdemeshev (обсуждение | вклад) |
Bdemeshev (обсуждение | вклад) (→Log Book) |
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Lecture. Conditional expected value, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l4.pdf pdf] | Lecture. Conditional expected value, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l4.pdf pdf] | ||
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| + | '''Week 5. 2023-09-16''' | ||
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| + | Practice. Conditional expected value and variance, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-05.pdf pdf] | ||
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| + | Lecture. | ||
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| + | '''Week 6. 2023-09-16''' | ||
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| + | Practice. Sigma-algebras, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-06.pdf pdf] | ||
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| + | Lecture. | ||
==Sources of Wisdom== | ==Sources of Wisdom== | ||
Версия 18:34, 13 октября 2023
Содержание
General course info
Lecturer: Peter Lukianchenko
Practice and problem solving: Boris Demeshev, Friday 16:20-17:40 Moscow time, zoom
Class teacher: Sveta Popova, Maria Kirillova
Log Book
Semester I: Stochastic Processes
Practice playlist Week 0. 2023-09-02
Lecture. Markov chains, transition matrix, pdf
Week 1. 2023-09-04
Class. Transition matrix, first step analysis, pdf by Maria
Practice. MGF and first step analysis, pdf
Lecture. Markov chains, classification of states, pdf
More:
Cambridge course on Markov chains
Week 2. 2023-09-11
Practice. More generating functions and first step analysis, pdf
Lecture. Convergence. pdf
Week 3. 2023-09-16
Lecture + practice. Poisson process, pdf, notes in progress
Class. Stationary distribution, convergence, pdf by Maria
More:
Inspection paradox, Waiting time paradox, Friendship paradox
Week 4. 2023-09-16
Practice. Convergence, pdf
Lecture. Conditional expected value, pdf
Week 5. 2023-09-16
Practice. Conditional expected value and variance, pdf
Lecture.
Week 6. 2023-09-16
Practice. Sigma-algebras, pdf
Lecture.
Sources of Wisdom
- all past exams
- a lot of problems... (under construction)
- TG chat 2023-24
- Statistics cookbook
- Wiki 2020-21, Wiki 2021-22, Wiki 2022-23
MC + MCMC
- 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
