Heike Siebert

siebert@mi.fu-berlin.de

Arnimallee 7, Raum 103

Office hours (during lecture time): Tuesday, 11:30-12:30 Bernhard Renard

RenardB@rki.de

Robert Koch-Institut, Nordufer 20, Raum 312

Office hours: after the lecture and by email appointment Alena van Bömmel

mysickal@zedat.fu-berlin.de

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.

SWS: 2

Exercises: Alena van Bömmel

SWS: 2

ECTS: 6

Language: English/German

Tuesday 16-18h at Arnimallee 6, 025/026

First lecture 15.10.

Exercises:

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

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

Mathematical background for Markov chains and related topics.

B. Renard:

Computational Statistics and Statistical Learning

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. http://www-stat.stanford.edu/~tibs/ElemStatLearn/download.html

The provided lecture 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)

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)

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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