Stochastic analysis 2019 2020

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 2019/2020 year, mandatory one for 1st year master students of Statistical Learning Theory program (HSE and Skoltech).

Telegram group

Chat for course-related discussions in telegram

• Link

Grading formula

The final grade consists of 3 components (each is non-negative real number from 0 to 10, without any intermediate rounding) :

• O_HW for the hometasks
• O_Mid-term for the midterm exam
• O_Exam for the final exam

The formula for the final grade is

• O_Final = 0.3*O_HW + 0.3*O_Mid-term + 0.4*O_Exam

with the usual (arithmetical) rounding rule.

Lectures

• Lecture 07.09
• Lecture 21.09
• Lecture 28.09
• Lecture 05.10
• Lecture 12.10
• MCMC-demo

Seminars

• To be filled

Midterm

Midterm will be held on 26.10.2019 at 10:30 in oral form. Each student will receive a theoretical question from the list of questions and a problem. While preparing the answer, any materials (notes, books, laptops, etc) are allowed. After 1 hour of preparation the oral part starts, during the answer using any materials is strictly prohibited. During the answer additional questions on the course may be asked, not necessarily related with the question from exam variant. Examinator could also ask you to solve some more problems on the course topics.

• List of questions

Consultation to midterm

Consultation will take place on 23.10 at 18:00 at room R609

Hometasks

• Homework №1, deadline - 12.10.2019, 23:59
• Homework №2, deadline - 06.11.2019, 23:59
• Homework №3, deadline - 01.12.2019, 23:59, Explicit recurrence for №4, do not open until emergency

Deadline postponed by 1 day, moderate hint added to file HW №3, shameless hint in the separate file

• Homework №4, deadline - 21.12.2019, 23:59
• Bonus hometask, deadline - 21.12.2019, 23:59

Grades and results

• Results

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

Recommended literature (2nd term)

• https://link.springer.com/book/10.1007%2F978-1-4419-9634-3 - Probability for Statistics and Machine Learning by A. Dasgupta, chapter 14
• https://link.springer.com/book/10.1007/978-3-540-68829-7 - Probability theory and Random Processes by L. Koralov and Y. Sinai, lecture 13 (Conditional expectations and martingales)