Просмотр исходного текста страницы Tssp-2024-25

== What-about ==

Course [https://github.com/bdemeshev/hse_panda_metrics_2024_2025/raw/main/whitepaper.pdf whitepaper]

=== Course goals ===

侍には目標がなく道しかない [Samurai niwa mokuhyō ga naku michi shikanai]

A samurai has no goal, only a path.

Telegram [https://t.me/+qQ48C4bmlNUzNTc6 chat]

== Grading ==

Stochastic Processes = 0.35 Halloween Exam + 0.40 Ded Moroz Exam + 0.25 Home Assignments

Time Series Analysis = 0.30 Mimoza Exam + 0.45 Sakura Exam + 0.25 Home Assignments

=== Home assignments ===

[https://github.com/bdemeshev/hse_panda_tssp_2024_2025/raw/main/home_assignments/home_assignments.pdf Home assignments :)]

Home assignments have equal weights. You have 4 honey weeks for the whole year.

=== Exams ===

[https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf Past exams]

== Samurai diary ==

Lecture slides and class [https://github.com/bdemeshev/hse_panda_tssp_2024_2025/tree/main/course_notes notes]

2024-09-


=== Classes  ===

Class [https://e.pcloud.link/publink/show?code=kZDCKPZ6dPB3lXGHrhUzqeC7wkVfyaLsAq7 video recordings]

2024-09-06: First step analysis, problem 1.1 from [https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf StoPro].

More on first step analysis: section 2.7.2 in [https://projects.iq.harvard.edu/stat110/home In2Pro]

2024-09-13: First step analysis, problems  from [https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf StoPro].

== Sources of Wisdom ==

[https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf StoPro]: Problems in Stochastic Processes

[https://projects.iq.harvard.edu/stat110/home In2Pro]: Blitstein, Hwang, Introduction to probability.

[https://aditya-sengupta.github.io/expository/markovtex.pdf MarkovTex]: Representing Markov Chains in Latex.

[https://www.statslab.cam.ac.uk/~rrw1/markov/M.pdf Mchains] Cambridge lectures on Markov chains.

[https://www.stat.berkeley.edu/~aldous/150/takis_exercises.pdf Takis]: Takis Konstantinopulos, One hundred solved exercises on Markov chains.