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Wilkommen beim Wiki "Statistik", WS 2013/2014

Heike Siebert
Arnimallee 7, Raum 103
Office hours (during lecture time): Tuesday, 11:30-12:30

Bernhard Renard
Robert Koch-Institut, Nordufer 20, Raum 312
Office hours: after the lecture and by email appointment

Alena van Bömmel


The results for the alternate exam are available here.
You have a chance to look at your graded exam on April 16, 9:30am, in Arnimallee 7, R103.
The final exam is on Feb 18, 10 am in Hörsaal A, Arnimallee 22.
The results are available here
The alternate exam is on Apr 08, 10 am in Hörsaal 001, Arnimallee 3. This is a closed book exam: no notes, books, cheat sheets, calculators, smart phones etc. are allowed.
Please bring a pen and your student ID.
The tentative (please check here!) time for looking at your graded exam is Feb 19, 16.30 in Arnimallee 7, R103 (Heike Siebert's office).
Please sign up on Campus Management BEFORE the exam if you want to take the exam

Those interested in a statistical bioinformatics seminar, please indicate your prefered time slot here.

Lecture starts on October 15, exercises on October 23.

General Informationen

Lecturers: Bernhard Renard, Heike Siebert
SWS: 2
Exercises: Alena van Bömmel
SWS: 2

Language: English/German

Dates and Locations

Tuesday 16-18h at Arnimallee 6, 025/026
First lecture 15.10.

Wednesday 12:15 - 13:45, Arnimallee 6, r. 031
Wednesday 14:15 - 15:45, Arnimallee 6, r. 032


H. Siebert:
Mathematical background for Markov chains and related topics.
B. Renard:
Computational Statistics and Statistical Learning


Exercises are mandatory, problem sets will be posted on this website on a weekly basis and are to be handed in at the Tuesday lecture. At least 50% of all graded problems need to be passed for a successfull participation. Attending the lecture is highly recommended. A 90 minute final examination determines the final grade.


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.

Hastie, Tibshirani & Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009.

Lecture Materials

Part 1
The provided lecture notes do not constitute a complete script. Proofs, examples, remarks etc. presented in the lecture might be cut in part or even completely. However, all important definitions and theorems can be found in the notes.

01 Introduction and basic definitions (notes)

02 Simulation, n-step transitions (notes)

03 Communication, periodicity (notes see above)

04 Recurrence, transience, absorption (notes)

05 Absorption cont. (notes)

06 Stationarity, ergodicity, reversibility (notes, also see notes above)

07 Markov Chain Monte Carlo, Hard Core model (notes)

08 Metropolis-Hastings, observables (notes) Note: in the definition of the acceptance function for the Metropolis-Hastings Algo the matrix S needs to be symmetric!

Part 2
01 Introduction (slides)

02 Non-parametric Testing (slides)(reading material)

03 Kernel Density Estimation (slides)(reading material)

04 Non-Parametric Regression (slides)(reading material)

05 Support Vector Machine (slides)(reading material)

06 Model Evaluation (slides)(reading material)

07 Classification and Regression Trees (slides)(reading material)

08 Bagging and Random Forests (slides)(reading material)

09 Boosting (slides)(reading material)

10 Method Overview (slides)


Problem sheet 1
To be handed in on October 29 in the lecture.

Problem sheet 2

Problem sheet 3

Problem sheet 4

Problem sheet 5

Problem sheet 6

Problem sheet 7

Problem sheet 8 R Introduction fly data

Problem sheet 9 protein data tumor data

Problem sheet 10 ehec data

Problem sheet 11 Patient1 and Patient2

Problem sheet 12 Chlamydia genomes

Problem sheet 13
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