MC 2023 — различия между версиями
Материал из Wiki - Факультет компьютерных наук
(Новая страница: «== Lecturers and Seminarists == {| class="wikitable" style="text-align:center" |- || Lecturer || [https://www.hse.ru/org/persons/219484540 Samsonov Sergey ] || […») |
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[... '''Table with grades'''] | [... '''Table with grades'''] | ||
| − | == Lectures | + | == Lectures == |
| − | *[https:// | + | * [https://disk.yandex.ru/i/LsKhx0pCNo5rag Lecture №1, 09.11] |
| + | * [https://disk.yandex.ru/i/gm4ch7k5ollvFw Lecture №2, 18.11] | ||
| + | * ... Lecture №3, 25.11] | ||
==Homeworks == | ==Homeworks == | ||
Версия 12:13, 18 ноября 2023
Содержание
Lecturers and Seminarists
| Lecturer | Samsonov Sergey | [svsamsonov@hse.ru] | T902 |
| Seminarist | Artur Goldman | [...] | T926 |
About the course
This page contains materials for Markov Chains course in 2023/2024 year, mandatory one for 1st year Master students of the MML 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 = 0.35*OHW + 0.3*OMid-term + 0.35*OExam
with the usual (arithmetical) rounding rule.
[... Table with grades]
Lectures
- Lecture №1, 09.11
- Lecture №2, 18.11
- ... Lecture №3, 25.11]
Homeworks
TBD
Exam
TBD
Midterm
TBD
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://elearning.unimib.it/pluginfile.php/583708/mod_resource/content/1/1-conditional-law.pdf - Probability kernels and (regular) conditional probabilities, to the third lecture.
