Willkommen beim Wiki "Statistik", WS 2015/16

Contact

Susanna Röblitz
susanna.roeblitz@zib.de
ZIB, Takustraße 7, Raum 4104
Office hours: by email appointment

Annalisa Marsico
annalisa.marsico@fu-berlin.de
Takustr. 9, Raum 011
Office hours: by email appointment

Alena van Bömmel
mysickov@molgen.mpg.de
MPI, Ihnestraße 63-73, Raum 3.3.83

Anna Ramisch
ramisch@molgen.mpg.de
MPI, Ihnestraße 63-73, Raum 3.3.83

Lisa Barros de Andrade e Sousa
lisasous@molgen.mpg.de
MPI, Ihnestraße 63-73, Raum 3.3.83

News

According to the information we got from the "Prüfungsbüro", there is no "Freischussregelung" any longer. That means, if you passed the first exam, you are not allowed to take the second one in order to improve your mark. This applies to all lectures that are described in the study and examination rules of the BSc and MSc Bioinformatics as well as the MSc Mathematics.

20.4. Here the results from the Nachklausur Results_Exam. To look at your graded exam please come to Annalisa Marsico's office (but write an email before).

Exam

The exam on February 11, 12:15-13:45, takes place in the ZIB Lecture Hall, Takustr. 7.
The 2nd exam takes place on April 14, 12:15-13:45, also in the ZIB Lecture Hall.

General Informationen

Lecturers: Annalisa Marsico, Susanna Röblitz
SWS: 2
Exercises: Alena van Bömmel, Lisa Barros de Andrade y Sousa, Anna Ramisch
SWS: 2
ECTS: 6
Language: English

Dates and Locations

Lecture:
Thursday 12-14h, Takustr. 9, SR 006/T9
First lecture 15.10.

Exercises:
Monday 12:15 - 13:45, Arnimallee 6, SR 032
Monday 14:15 - 15:45, Arnimallee 6, SR 025/026

Topics

S. Röblitz:
Mathematical background for Markov chains and related topics.
A. Marsico:
Computational Statistics and Statistical Learning

Requirements

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 the graded problems in each of the two parts need to be passed for a successfull participation. Attending the lecture is highly recommended. A 90 minute final examination determines the final grade.

Literature

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.
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. Available here.

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 Canonical representation and n-step transition. Notes
03 Communication and periodicity (notes see above).
04 Recurrence and transience, absorption. Notes
05 Absorption, ergodicity. Notes
06 Ergodicity, reversibility. Notes
07 Markov Chain Monte Carlo, Metropolis-Hastings Algorithm. Notes

Part 2
The provided slides do not constitute a complete script. Proofs, examples, remarks etc. presented in the lecture on the board may be missing. However, all important definitions and theorems can be found in the slides. Further, additional reading material is provided which can help understanding the topics from a different perspective. The additional notes may exceed the material presented in the lecture (Only what was covered in the lecture is part of the final exam).

01 Non-parametric tests SLIDES Notes
02 Normalization SLIDES Notes article upper_quantile
03 Kernel Density Estimation SLIDES Notes - chapter2
04 Nonparametric Regression SLIDES
05 Support Vector Machines SLIDES additional_notes
06 Model Evaluation SLIDES
07 Classification and Regression trees SLIDES
08 Review notes_annalisa Task3 Task4 notes_Anna

Exercises

Homework 1
Homework 2
Homework 3
Homework 4
Homework 5
Homework 6

Homework 7 additional_notes
Homework 8 dataset
Homework 9 protein_data tumor_data
Homework 10 ehec_data
Homework 11 patient_data
Homework 12 data1 data2
Homework 13 patient_data ehec_data