Tssp-2022-23 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
Bdemeshev (обсуждение | вклад) |
Bdemeshev (обсуждение | вклад) (→Sources) |
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* [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22] | * [https://github.com/bdemeshev/tssp/tree/master/2020_2021 Git repo 2020-21], [https://github.com/bdemeshev/tssp_2021-22/ Git repo 2021-22] | ||
* [https://t.me/TSSP22 TG chat 2022-23] | * [https://t.me/TSSP22 TG chat 2022-23] | ||
| + | * [https://www.youtube.com/playlist?list=PLGpdGKp2JUvygvPGgYZLNoC82Ug8Fd81L видео семинаров 2022-23 на русском] | ||
* [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook] | * [https://github.com/mavam/stat-cookbook/releases/download/0.2.6/stat-cookbook.pdf Statistics cookbook] | ||
Версия 21:47, 9 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
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
