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

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== General course info ==
 
== General course info ==
  
* Boring [https://www.hse.ru/edu/courses/383218629 Official] web page
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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]
  
 
== Week progress ==
 
== Week progress ==
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==== Week 01 ====
 
==== Week 01 ====
  
* Sigma-algebras, measurability of random variable with respect to sigma-algebra
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* Sigma-algebras, measurability of random variable with respect to sigma-algebra.
* HA-01
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* Seminar 01
  
 
==== Week 02 ====
 
==== Week 02 ====
  
 
* Markov chain. Classification of states. Calculations of return probability, mean return time, stationary distribution.
 
* 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.
  
* Cambridge [http://www.statslab.cam.ac.uk/~rrw1/markov/ Markov chain course]
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==== Week 03 ====
  
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* Conditional expected value. Martingales.
  
==== Week 03 ====
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==== Week 04 ====
  
* Conditional expected value. Martingales.  
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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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=== 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]

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)