Heike Siebert

Arnimallee 7, Raum 103

Office hours (during lecture time): Wednesday, 11:30-12:30 ( not on February 6 2013 ) Bernhard Renard

Robert Koch-Institut, Nordufer 20, Raum 312

Office hours: after the lecture and by email appointment Natasa Djurdjevac

Arnimallee 6, Raum 127

Email: djurdjev[at]mi.fu-berlin.de

You can have a look at your exam on

The results for the exam are online. (If your result is missing, you did not grant us permission to publish your results on the web - please send an email with your exam number)

On February 26, 16:30-17:30, you can come by and have a look at your exam in Arnimallee 7, R103 (Heike Siebert's office). You can also take this opportunity to have a look at the exam if you did not write the exam on Feb. 19. Please send an email if you want to take that opportunity.

Problem set 14 will be a bonus sheet. You can use it to collect some more points if you did not make the 50% on sheets 1-13. The topic will be MCMC so you might want to have a look at the type of problems regardless!

There will be no lecture on Feb 12. However, starting at 9:00 we will give a short review of the lecture content and answer questions concerning the exam. If you have any specific questions to some lecture topic please email us your question in advance, so we can deal with it efficiently.

Registration at Campus Management should be possible now. Please register!

As announced, we are not allowed to offer a full 'Freischuss' (the possibility to retake the exam and choose the better grade from either exam). If you passed the exam on Feb 19 but are not happy with your grade, you can contact us and ask for your exam to count as a failing grade and retake the exam on April 9. Be sure to understand, however, that such a decision to have the exam evaluated as a failing grade is not retractable!

No aids are allowed. You just need a pen, we'll provide paper.

In Bioinformatics there is no "Freischuss". For those who did not pass, there will be another exam (probably on April 9 18-20).

SWS: 2

Exercises: Natasa Djurdjevac

SWS: 2

ECTS: 6

Sprache: English/Deutsch

8:30 - 10:00 on Tuesdays in room 031, Arnimallee 6

Start: 16.10.

Exercises:

12:15 - 13:45 on Wednesdays in room 119, Arnimallee 3

14:15 - 15:45 on Wednesdays in room 130, Arnimallee 3 (backbuilding)

Start: 24.10.

Computational Statistics and Statistical Learning

H. Siebert:

Mathematical background for Markov chains and related topics.

Volker Schmidt. Markov Chains and Monte-Carlo Simulation, Lecture Notes University Ulm, 2010. Available here.

Pierre Bremaud. Markov Chains, Gibbs Fields, Mote Carlo Simulation, and Queues. Springer 1999. Available here.

Ehrhard Behrends, Introduction to Markov Chains (with Special Emphasis on Rapid Mixing), Vieweg, 1999.

01 Introduction: Slides

02 Multiple Regression: Slides Lecture Notes

03 Kernel Density Estimation: Slides Lecture Notes

04 Non-Parametric Regression: Slides Lecture Notes

05 Model Evaluation: Slides Lecture Notes

06 Regularization: Slides Lecture Notes

07 Classification Trees: Slides Lecture Notes

08 Ensemble Methods: Slides Lecture Notes

09 Normalization: Slides

10 Summary: Slides

Part 2

01 Basic Definitions (References: Schmidt Section 2.1.1 and Ex 1 of 2.1.2, Bremaud Chapter 2.1 ):

stochastic process, homogeneous Markov chain, transition matrix, transition graph

02 Canonical representation, n-step transition and stationarity; notes

03 Communication and absorption; notes

04 (A)Periodicity; notes

05 Ergodicity and stationarity; notes

06 Reversibility; Introduction to Markov Chain Monte Carlo; notes

07 Markov Chain Monte Carlo: Metropolis-Hastings-Algorithm; notes

Exercise 1 (due Oct 30 in the lecture) solution

Exercise 2 protein data tumor data (due Nov 06 in the lecture) solution

Exercise 3 ehec data (due Nov 13 in the lecture) solution

Exercise 4 chlamydia psittaci genome chlamydia trachomatis genome (due Nov 20 in the lecture) solution

Exercise 5 spectrum models (due Nov 27 in the lecture) solution

Exercise 6 data (due Dec 04 in the lecture) solution

Exercise 7 (due Dec 11 in the lecture)

Exercise 8 (due Dec 18 in the lecture)

Exercise 9 (due Jan 8 in the lecture)

Exercise 10 (due Jan 15 in the lecture)

Exercise 11 (due Jan 22 in the lecture)

Exercise 12 (due Jan 29 in the lecture)

Exercise 13 (due Feb 5 in the lecture)

Exercise 14 (bonus sheet, due Feb 12 in the lecture)

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