Stochastic analysis 2021 2022 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
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== Seminars == | == Seminars == | ||
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*[https://www.dropbox.com/s/xvf1x6v2frm3k9c/Seminar_11_09_stochan.pdf?dl=0 '''Seminar 11.09'''] | *[https://www.dropbox.com/s/xvf1x6v2frm3k9c/Seminar_11_09_stochan.pdf?dl=0 '''Seminar 11.09'''] | ||
| + | *[https://www.dropbox.com/s/i5g7a1pnbsnwclm/Seminar_18_09.pdf?dl=0 '''Seminar 18.09'''] | ||
| + | *[https://www.dropbox.com/s/xctvce7ojtfcxrm/Seminar_02_10_stochan_1st_part.pdf?dl=0 '''Seminar 02.10 (first part, Gambler's ruin was not discussed here)'''][https://www.dropbox.com/s/3asrxd56bbljkii/Martingales.pdf?dl=0 '''Seminar 02.10 (second part, Dooob's optional sampling theorem (was provided without the proof) and Doob's maximal inequality)'''] | ||
==Homeworks == | ==Homeworks == | ||
Версия 00:40, 10 ноября 2021
Содержание
Lecturers and Seminarists
| Lecturer | Naumov Alexey | [anaumov@hse.ru] | T924 |
| Seminarist | Samsonov Sergey | [svsamsonov@hse.ru] | T926 |
About the course
This page contains materials for Stochastic Analysis course in 2021/2022 year, mandatory one for 1st year Master students of the Statistical Learning Theory program (HSE and Skoltech).
Grading
The final grade consists of 3 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :
- OHW for the hometasks
- OMid-term for the midterm exam
- OExam for the final exam
The formula for the final grade is
- OFinal = 0.3*OHW + 0.3*OMid-term + 0.4*OExam + 0.1*OBonus HW
with the usual (arithmetical) rounding rule.
Lectures
Seminars
- Seminar 11.09
- Seminar 18.09
- Seminar 02.10 (first part, Gambler's ruin was not discussed here)Seminar 02.10 (second part, Dooob's optional sampling theorem (was provided without the proof) and Doob's maximal inequality)
Homeworks
Exam
Midterm
Midterm will take place on Saturday, 13.11.2020, at 11:00. Midterm is open-book, all materials are allowed. Midterm will take 3 hours and it will contain 6 problems. Solving any 5 of them will give you the maximal grade.
Recommended literature (1st term)
- http://www.statslab.cam.ac.uk/~james/Markov/ - Cambridge lecture notes on discrete-time Markov Chains
- https://link.springer.com/book/10.1007%2F978-3-319-97704-1 - book by E. Moulines et al, you are mostly interested in chapters 1,2,7 and 9 (book is accessible for download through HSE network)
- https://link.springer.com/book/10.1007%2F978-3-319-62226-2 - Stochastic Calculus by P. Baldi, good overview of conditional probabilities and expectations (part 4, also accessible through HSE network)
- https://link.springer.com/book/10.1007%2F978-1-4419-9634-3 - Probability for Statistics and Machine Learning by A. Dasgupta, chapter 19 (MCMC), also accessible through HSE network
