Stochastic analysis 2021 2022 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
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== Exam == | == Exam == | ||
| + | *[https://www.dropbox.com/s/aev6kyohfb2a0kd/Questions_stochan_2021_Final.pdf?dl=0 '''List of questions'''] | ||
==Midterm == | ==Midterm == | ||
Версия 23:59, 15 декабря 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)
- 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.
