Time Series and Stochastic Processes ada 20 21 — различия между версиями

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(не показано 10 промежуточных версии этого же участника)
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== General course info ==
  
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* Boring [https://www.hse.ru/edu/courses/383218629 official] web page
  
 
* [https://t.me/joinchat/DtwHDEbRczyglTC1Z-W-Ug tg-channel]
 
* [https://t.me/joinchat/DtwHDEbRczyglTC1Z-W-Ug tg-channel]
  
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* [https://github.com/bdemeshev/tssp/raw/master/ha/tssp_ha.pdf All home Assignments]
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== Week progress ==
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==== Week 01 ====
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* Sigma-algebras, measurability of random variable with respect to sigma-algebra.
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* Seminar 01
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==== Week 02 ====
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* Markov chain. Classification of states. Calculations of return probability, mean return time, stationary distribution.
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* Seminar 02a, [https://youtu.be/WBKkk0iqysU?list=PL1poMUvVlAqfu6D4gaA_c4fJiIfsWbjzV 02b]
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* Cambridge [http://www.statslab.cam.ac.uk/~rrw1/markov/ Markov chain course]. There you may find useful: [http://www.statslab.cam.ac.uk/~rrw1/markov/M.pdf lecture notes], [http://www.statslab.cam.ac.uk/~rrw1/markov/MarkovChainTriposQuestions.pdf past tripos] and more.
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==== Week 03 ====
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* Conditional expected value. Martingales.
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==== Week 04 ====
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* [https://youtu.be/mJmbcp5h7lo?list=PL1poMUvVlAqfu6D4gaA_c4fJiIfsWbjzV Abracadabra martingale]
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==== Week 05 ====
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* Wiener process: basic properties, inversion
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==== Week 06 ====
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* Wiener process: limit in L2
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==== Week 07 ====
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[https://www.youtube.com/watch?v=yTCI-Ng76OU Ito integral WtdWt], Ito's lemma
  
 
== Sources ==
 
== Sources ==
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* [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]
 
* [https://github.com/bdemeshev/sc401/raw/master/sc_pset/sc_problems_main.pdf Черновик задачника (рус)]
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=== Time Series ===
 
=== Time Series ===
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* [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]
 
* [https://robjhyndman.com/uwafiles/9-StateSpaceModels.pdf Rob Hyndman, State Space Models]
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=== MC + MCMC ===
 
=== MC + MCMC ===
 
* James Norris, Markov chains (1998, no kernels)
 
* James Norris, Markov chains (1998, no kernels)
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* [http://www.statslab.cam.ac.uk/~rrw1/markov/ Cambridge course] on Markov chains
  
 
* [https://eml.berkeley.edu/reprints/misc/understanding.pdf Chib and Greenberg, Understanding MH algorithm]
 
* [https://eml.berkeley.edu/reprints/misc/understanding.pdf Chib and Greenberg, Understanding MH algorithm]
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* [http://www.statslab.cam.ac.uk/~rrw1/markov/index.html  (no kernels)]
 
* [http://www.statslab.cam.ac.uk/~rrw1/markov/index.html  (no kernels)]
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* [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains by Roberts and Rosenthal (+++, статья)]
 
* [https://projecteuclid.org/euclid.ps/1099928648 General State Space Markov Chains by Roberts and Rosenthal (+++, статья)]

Версия 17:30, 15 октября 2020

General course info

Week progress

Week 01

  • Sigma-algebras, measurability of random variable with respect to sigma-algebra.
  • Seminar 01

Week 02

  • Markov chain. Classification of states. Calculations of return probability, mean return time, stationary distribution.
  • Seminar 02a, 02b
  • Cambridge Markov chain course. There you may find useful: lecture notes, past tripos and more.

Week 03

  • Conditional expected value. Martingales.

Week 04

Week 05

  • Wiener process: basic properties, inversion

Week 06

  • Wiener process: limit in L2

Week 07

Ito integral WtdWt, Ito's lemma

Sources

Stochastic Calculus

  • Zastawniak, Basic Stochastic Processes

Time Series

UCM

MC + MCMC

  • James Norris, Markov chains (1998, no kernels)
Источник — «http://wiki.cs.hse.ru/index.php?title=Time_Series_and_Stochastic_Processes_ada_20_21&oldid=46154»