Tssp-2022-23 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Bdemeshev (обсуждение | вклад) (→Log-book) |
Bdemeshev (обсуждение | вклад) |
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== Log-book == | == Log-book == | ||
| − | Week 1. 2022-09-03 | + | '''Week 1. 2022-09-03''' |
Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf] | Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l1_DSBA3_2022.pdf pdf] | ||
| Строка 27: | Строка 27: | ||
[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 | ||
| − | Week 2. 2022-09-10 | + | '''Week 2. 2022-09-10''' |
Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf] | Lecture. Markov chains, stationary distribution, modes of convergence, [https://github.com/bdemeshev/tssp_2022-23/raw/main/lectures/TSSP_m1_l2_DSBA3_2022.pdf pdf] | ||
Версия 21:54, 10 сентября 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
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
Sources
- 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
