Stochastic analysis 2020 2021 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
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* [https://www.dropbox.com/s/4q3l1u6os9jtqml/Lecture_12_09.pdf?dl=0 '''Lecture 12.09'''] | * [https://www.dropbox.com/s/4q3l1u6os9jtqml/Lecture_12_09.pdf?dl=0 '''Lecture 12.09'''] | ||
* [https://www.dropbox.com/s/pjrtvkia3zu8tx6/Lecture_19_09.pdf?dl=0 '''Lecture 19.09'''] | * [https://www.dropbox.com/s/pjrtvkia3zu8tx6/Lecture_19_09.pdf?dl=0 '''Lecture 19.09'''] | ||
| − | * [https://www.dropbox.com/s/ | + | * [https://www.dropbox.com/s/3asrxd56bbljkii/Martingales.pdf?dl=0 '''Lecture-Seminar 26.09, part 1 (Martingales)'''] |
| − | * [https://www.dropbox.com/s/vqvzh7vb1cq96l7/Gaussian%20Process.pdf?dl=0 '''Lecture-Seminar 26.09, part 2 ( | + | * [https://www.dropbox.com/s/vqvzh7vb1cq96l7/Gaussian%20Process.pdf?dl=0 '''Lecture-Seminar 26.09, part 2 (Wiener process)'''] |
== Seminars == | == Seminars == | ||
Версия 20:14, 26 сентября 2020
Содержание
Lecturers and Seminarists
| Lecturer | Naumov Alexey | [anaumov@hse.ru] | T924 |
| Lecturer | Belomestny Denis | [dbelomestny@hse.ru] | T926 |
| Seminarist | Samsonov Sergey | [svsamsonov@hse.ru] | T926 |
About the course
This page contains materials for Stochastic Analysis course in 2020/2021 year, mandatory one for 1st year Master students of the Statistical Learning Theory program (HSE and Skoltech).
Grading formula
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
with the usual (arithmetical) rounding rule.
Lectures
- Lecture 05.09
- Lecture 12.09
- Lecture 19.09
- Lecture-Seminar 26.09, part 1 (Martingales)
- Lecture-Seminar 26.09, part 2 (Wiener process)
Seminars
- To be filled
Midterm
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
