Tssp-2022-23 — различия между версиями
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'''Week 4. 2022-09-24''' | '''Week 4. 2022-09-24''' | ||
| − | Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/ | + | Lecture. Conditional expectation, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l4_DSBA3_2022.pdf pdf] |
Class. Conditional expectation, sigma algebra | Class. Conditional expectation, sigma algebra | ||
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Class. Conditional expectation and variance, sigma algebra | Class. Conditional expectation and variance, sigma algebra | ||
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| + | '''Week 6. 2022-10-08''' | ||
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| + | Lecture. Basics of stochastic processes [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l6_DSBA3_2022.pdf pdf] | ||
== Sources == | == Sources == | ||
Версия 12:59, 10 октября 2022
Содержание
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
Home assignments
Log-book
Week 1. 2022-09-03
Lecture. Markov chains, transition matrix, pdf
Class. Transition matrix, first step analysis.
More:
Cambridge course on Markov chains
Week 2. 2022-09-10
Lecture. Markov chains, stationary distribution, modes of convergence, pdf
Class. Stationary distribution, modes of convergence
Week 3. 2022-09-17
Lecture. Markov process, math modelling, pdf
Class. plim, almost sure lim
Week 4. 2022-09-24
Lecture. Conditional expectation, pdf
Class. Conditional expectation, sigma algebra
Week 5. 2022-10-01
Lecture. First-step analysis, sigma algebra pdf
Class. Conditional expectation and variance, sigma algebra
Week 6. 2022-10-08
Lecture. Basics of stochastic processes pdf
Sources
- all past exams
- Wiki 2020-21, Wiki 2021-22
- Git repo 2020-21, Git repo 2021-22
- TG chat 2022-23
- видео семинаров 2022-23 на русском
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
