Stochastic analysis 2019 2020 — различия между версиями
| Строка 51: | Строка 51: | ||
*[https://docs.google.com/spreadsheets/d/1ApuKS3flYKdhKiL_fVPCDnUumfOlrh9SYuZ79LRRpN4/edit?usp=sharing Results] | *[https://docs.google.com/spreadsheets/d/1ApuKS3flYKdhKiL_fVPCDnUumfOlrh9SYuZ79LRRpN4/edit?usp=sharing Results] | ||
| − | == Recommended literature == | + | == 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 | ||
| + | |||
| + | == 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) | ||
Версия 15:29, 10 ноября 2019
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 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
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
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
Midterm retake (for those who missed the first date due to an acceptable reason) will be organised on 16.11.2019, after the seminar.
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
Note that there was a typo in the description of MALA, now it is fixed
Deadline postponed for 24 hours
Grades and 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)
