Stochastic analysis 2021 2022 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
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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/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)'''] | + | *[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)'''] |
+ | *[https://www.dropbox.com/s/eckkmpt0sjb1dss/Seminar_09_10_martingales.pdf?dl=0 '''Seminar 09.10 (fist part, martingales)'''], [https://www.dropbox.com/s/uit5h98bvopucso/Gaussian%20Process_2022.pdf?dl=0 '''Seminar 09.10 (secoond part, Wiener process)'''] | ||
==Homeworks == | ==Homeworks == |
Версия 01:53, 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)
- Seminar 09.10 (fist part, martingales), Seminar 09.10 (secoond part, Wiener process)
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