Stochastic analysis 2021 2022 — различия между версиями
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Mednik (обсуждение | вклад) м (Откат правок Seosky (обсуждение) к версии Svsamsonov) |
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| (не показано 25 промежуточных версии 3 участников) | |||
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== Lectures == | == Lectures == | ||
*[https://www.dropbox.com/s/xvf1x6v2frm3k9c/Seminar_11_09_stochan.pdf?dl=0 '''Lecture 11.09'''] | *[https://www.dropbox.com/s/xvf1x6v2frm3k9c/Seminar_11_09_stochan.pdf?dl=0 '''Lecture 11.09'''] | ||
| + | *[https://www.dropbox.com/s/ejpfy7b2fl1yhq9/Stochastic%20Analysis%20Poincare%20Inequality_old.pdf?dl=0 '''Lecture 27.11'''] | ||
| + | *[https://www.dropbox.com/s/6c9yw85h2kz2eag/Stochastic%20Analysis%20Poincare%20Inequality.pdf?dl=0 '''Lecture 11.12'''] | ||
== Seminars == | == Seminars == | ||
*[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)'''] | ||
| + | *[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)'''] | ||
| + | *[https://www.dropbox.com/s/ttdenija8cf3do1/Seminar_20_11.pdf?dl=0 '''Seminar 20.11'''][https://www.dropbox.com/s/nisy81gasxcz6ru/Seminar_20_11_2021_stochastic_analysis.mp4?dl=0 '''Seminar 20.11 (video)'''] | ||
| + | *[https://www.dropbox.com/s/1bhlvr2h5ul2exw/Seminar_27_11.pdf?dl=0 '''Seminar 27.11'''][https://www.dropbox.com/s/d9uw7epg8429wfw/Seminar_27_11_stochatic_analysis.mp4?dl=0 '''Seminar 27.11 (video)'''] | ||
==Homeworks == | ==Homeworks == | ||
| − | *[https://www.dropbox.com/s/b8f1nyqnge6chw2/HW_1_stochan_2021.pdf?dl=0 | + | *[https://www.dropbox.com/s/b8f1nyqnge6chw2/HW_1_stochan_2021.pdf?dl=0'''Homework №1, deadline: 05.10.2021, 23:59'''] [https://www.dropbox.com/s/3oeztervc7d4nf1/HW_1_stochan_2021_hints.pdf?dl=0 '''Hints'''] |
| + | *[https://www.dropbox.com/s/ivasebriv46picb/HW_2_stochan_2021.pdf?dl=0'''Homework №2, deadline: 04.11.2021, 23:59'''][https://www.dropbox.com/s/voye6c0daa42bv0/HW_2_stochan_2021_hints.pdf?dl=0 '''Hints'''] | ||
| + | *[https://www.dropbox.com/s/rywaredtn30gejv/HW_3_stochan_2021.pdf?dl=0'''Homework №3, deadline: 12.12.2021, 23:59'''] | ||
== Exam == | == Exam == | ||
| + | *[https://www.dropbox.com/s/aev6kyohfb2a0kd/Questions_stochan_2021_Final.pdf?dl=0 '''List of questions'''] | ||
==Midterm == | ==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. | ||
| + | |||
| + | *[https://www.dropbox.com/s/gqeaq9vwonfex02/Questions_stochan_midterm_2021.pdf?dl=0 '''List of topics'''] | ||
| + | *[https://www.dropbox.com/s/1oxswu950umhtqw/consult_10_11_stochan.mp4?dl=0 '''Consultation, video'''], [https://www.dropbox.com/s/5pmn8i3yqzdkt6n/%D0%91%D0%BB%D0%BE%D0%BA%D0%BD%D0%BE%D1%82%20%D0%B1%D0%B5%D0%B7%20%D0%BD%D0%B0%D0%B7%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F%20%2838%29%20%282%29.pdf?dl=0 '''Consultation, notes'''] | ||
== Recommended literature (1st term) == | == Recommended literature (1st term) == | ||
| − | *http://www.statslab.cam.ac.uk/~james/Markov/ - Cambridge lecture notes on discrete-time Markov Chains | + | *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%2F978-3-319-97704-1 - 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%2F978-3-319-62226-2 - 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 | + | *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; |
| + | *http://th.if.uj.edu.pl/~gudowska/dydaktyka/Oksendal.pdf - Stochastic Differential Equations by Bernt Oksendal, chapters 3-4-5 provide a construction of the Stochastic integral and all required information on SDE's. | ||
Текущая версия на 09:35, 26 августа 2022
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)
- Seminar 20.11Seminar 20.11 (video)
- Seminar 27.11Seminar 27.11 (video)
Homeworks
- Homework №1, deadline: 05.10.2021, 23:59 Hints
- Homework №2, deadline: 04.11.2021, 23:59Hints
- Homework №3, deadline: 12.12.2021, 23:59
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;
- http://th.if.uj.edu.pl/~gudowska/dydaktyka/Oksendal.pdf - Stochastic Differential Equations by Bernt Oksendal, chapters 3-4-5 provide a construction of the Stochastic integral and all required information on SDE's.
