Tssp-2023-24: различия между версиями
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Bdemeshev (обсуждение | вклад) |
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| (не показано 14 промежуточных версий 2 участников) | |||
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Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] | Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] | ||
Practice and problem solving: Boris Demeshev, Friday 16:20-17:40 Moscow time, [zoom] | Practice and problem solving: [https://www.hse.ru/staff/bbd/ Boris Demeshev], Friday 16:20-17:40 Moscow time, [https://zoom.us/j/8126338383 zoom] | ||
Class teacher: [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova | Class teacher: [https://www.hse.ru/org/persons/14288706 Sveta Popova], [https://www.hse.ru/org/persons/785361814 Maria Kirillova] | ||
==Log Book == | |||
==== Semester I: Stochastic Processes ==== | |||
[https://www.youtube.com/playlist?list=PLnIS95ct9auXMX-4-ESGZvigU1w6kexw0 Practice playlist] | |||
[https://raw.githubusercontent.com/bdemeshev/tssp_2023-24/main/ha/tssp_ha.pdf home assignments] | |||
'''Week 0. 2023-09-02''' | |||
Lecture. Markov chains, transition matrix, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l1%202.pdf pdf] | |||
'''Week 1. 2023-09-04''' | |||
Class. Transition matrix, first step analysis, [https://github.com/bdemeshev/tssp_2023-24/raw/main/classes/HSE_sem1.pdf pdf by Maria] | |||
Practice. MGF and first step analysis, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-01.pdf pdf] | |||
Lecture. Markov chains, classification of states, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l2.pdf pdf] | |||
More: | More: | ||
| Строка 27: | Строка 32: | ||
[http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains | [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains | ||
'''Week 2. 2023-09-11''' | |||
Practice. More generating functions and first step analysis, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-02.pdf pdf] | |||
Lecture. Convergence. [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l3.pdf pdf] | |||
'''Week 3. 2023-09-16''' | |||
Lecture + practice. Poisson process, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-03.pdf pdf], [https://bdemeshev.github.io/tssp_2023-24/poisson-process.html notes in progress] | |||
Class. Stationary distribution, convergence, [https://github.com/bdemeshev/tssp_2023-24/raw/main/classes/HSE_sem3.pdf pdf by Maria] | |||
More: | |||
[https://towardsdatascience.com/the-inspection-paradox-is-everywhere-2ef1c2e9d709 Inspection paradox], [https://www.math.ucla.edu/~mason/papers/frym-WTP-published.pdf Waiting time paradox], [https://en.wikipedia.org/wiki/Friendship_paradox Friendship paradox] | |||
'''Week 4. 2023-09-16''' | |||
Practice. Convergence, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-04.pdf pdf] | |||
Lecture. Conditional expected value, [https://github.com/bdemeshev/tssp_2023-24/raw/main/lectures/TSSP_23_m1_l4.pdf pdf] | |||
'''Week 5. 2023-09-16''' | |||
Practice. Conditional expected value and variance, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-05.pdf pdf] | |||
Lecture. Sigma-algebras, [https://github.com/bdemeshev/tssp_2023-24/blob/main/lectures/TSSP_23_m1_l6_v2.pdf pdf] | |||
'''Week 6. 2023-09-16''' | |||
Practice. Sigma-algebras, [https://github.com/bdemeshev/tssp_2023-24/raw/main/practice/practice-06.pdf pdf] | |||
Lecture. | |||
==Sources of Wisdom== | ==Sources of Wisdom== | ||
* [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams] | * [https://github.com/bdemeshev/tssp_exams/raw/main/tssp_exams.pdf all past exams] | ||
* [ | * [https://github.com/bdemeshev/stochastic_pro/raw/main/stochastic_pro.pdf a lot of problems...] (under construction) | ||
* [https://t.me/spts2023 TG chat 2023-24] | * [https://t.me/spts2023 TG chat 2023-24] | ||
* [https://github.com/mavam/stat-cookbook/releases/download/0.2.7/stat-cookbook.pdf Statistics cookbook] | * [https://github.com/mavam/stat-cookbook/releases/download/0.2.7/stat-cookbook.pdf Statistics cookbook] | ||
* [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_20_21 Wiki 2020-21], [http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Wiki 2021-22], [http://wiki.cs.hse.ru/Tssp-2022-23 Wiki 2022-23] | |||
=== MC + MCMC === | === MC + MCMC === | ||
Текущая версия от 15:41, 13 октября 2023
General course info
Lecturer: Peter Lukianchenko
Practice and problem solving: Boris Demeshev, Friday 16:20-17:40 Moscow time, zoom
Class teacher: Sveta Popova, Maria Kirillova
Log Book
Semester I: Stochastic Processes
Week 0. 2023-09-02
Lecture. Markov chains, transition matrix, pdf
Week 1. 2023-09-04
Class. Transition matrix, first step analysis, pdf by Maria
Practice. MGF and first step analysis, pdf
Lecture. Markov chains, classification of states, pdf
More:
Cambridge course on Markov chains
Week 2. 2023-09-11
Practice. More generating functions and first step analysis, pdf
Lecture. Convergence. pdf
Week 3. 2023-09-16
Lecture + practice. Poisson process, pdf, notes in progress
Class. Stationary distribution, convergence, pdf by Maria
More:
Inspection paradox, Waiting time paradox, Friendship paradox
Week 4. 2023-09-16
Practice. Convergence, pdf
Lecture. Conditional expected value, pdf
Week 5. 2023-09-16
Practice. Conditional expected value and variance, pdf
Lecture. Sigma-algebras, pdf
Week 6. 2023-09-16
Practice. Sigma-algebras, pdf
Lecture.
Sources of Wisdom
- all past exams
- a lot of problems... (under construction)
- TG chat 2023-24
- Statistics cookbook
- Wiki 2020-21, Wiki 2021-22, Wiki 2022-23
MC + MCMC
- Cambridge course on Markov chains
- Chib and Greenberg, Understanding MH algorithm
- Casella, Explaining Gibbs Sampler
- Roberts and Rosenthal, General State Space Markov Chains
- Charles Geyer, MCMC lecture notes (with a little bit of kernels!)
Stochastic Calculus
- Zastawniak, Basic Stochastic Processes
Time Series
- Van der Vaart, Time Series
UCM
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter