Просмотр исходного текста страницы Stochastic analysis 2021 2022

== Lecturers and Seminarists ==

{| class="wikitable" style="text-align:center"
|-
|| Lecturer || [https://www.hse.ru/staff/anaumov Naumov Alexey ] || [anaumov@hse.ru] || T924
|- 
|| Seminarist || [https://www.hse.ru/org/persons/219484540 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) :
* O<sub>HW</sub> for the hometasks
* O<sub>Mid-term</sub> for the midterm exam
* O<sub>Exam</sub> for the final exam  
The formula for the final grade is 
* O<sub>Final</sub> = 0.3*O<sub>HW</sub> + 0.3*O<sub>Mid-term</sub> + 0.4*O<sub>Exam</sub> + 0.1*O<sub>Bonus HW</sub>
with the usual (arithmetical) rounding rule.

[https://docs.google.com/spreadsheets/d/1MPWVIkgxyotHU-P5cE7Gik4C6RTWxTnAVK8Btl7Fw3Y/edit?usp=sharing '''Table with grades''']

== Lectures ==
*[https://www.dropbox.com/s/xvf1x6v2frm3k9c/Seminar_11_09_stochan.pdf?dl=0 '''Lecture 11.09''']

== Seminars ==
*[https://www.dropbox.com/s/i5g7a1pnbsnwclm/Seminar_18_09.pdf?dl=0 '''Seminar 18.09''']
*[https://www.dropbox.com/s/xvf1x6v2frm3k9c/Seminar_11_09_stochan.pdf?dl=0 '''Seminar 11.09''']

==Homeworks ==
*[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''']

== Exam ==

==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