Instructor: Prof. Dr. Verena Wolf
Assistants: Alexander Andreychenko, Charalampos Kyriakopoulos
News: Midterm is on Dec 16, at 12:00.
Materials: CMS system
Schedule: Wed, 12:00 13:30, Building E2 1 – Room 007
Tutorial slot: Tue, 12:15 13:45, Building E2 1 – Room 007
Objectives:
The aim of this course is to give students of bioinformatics and computer science a solid background in probability and statistics as well as detailed knowledge about computer simulation of stochastic models, parameter estimation and hypothesis testing. Students will also learn how to use software tools such as R and Matlab to analyse data and probabilistic models. This course is a flipped classroom course which means that exercises are solved/presented during the two slots and the homework consists in watching videos which will be uploaded at the course homepage.
Prerequisites:
The course is open to students from bioinformatics and computer science. Mathematical skills as well as basic programming skills are of advantage but not mandatory.
Certification Conditions:
There will be two written exams. The assignments are optional but bonus points may be obtained during the tutorial.
Exam Schedule: tba
Using Matlab:
As a part of the assignments the participants will have to program in Matlab. Instructions on how to get access to Matlab.
Syllabus:
 PART I – REVIEW OF PROBABILITY AND RANDOM VARIABLES
 Events and their probabilities
 Rules of probability
 Combinatorics
 Conditional probability and independence
 Distribution of a random variable
 Expectation and variance
 Families of discrete distributions
 Families of continuous distributions
 PART II – COMPUTER SIMULATIONS AND STOCHASTIC PROCESSES
 Simulation of random variables
 Monte Carlo methods
 Markov chains
 Hidden Markov models
 PART III – INTRODUCTION TO STATISTICS AND STATISTICAL INFERENCE
 Simple descriptive and graphical statistics
 Parameter estimation
 Confidence intervals
 Hypothesis testing
 Chisquare tests
 tba
 Bayesian inference
Text Books:
 Probability and Statistics for Computer Scientists, Michael Baron, Taylor & Francis, 2013
 Simulation Modelling and Analysis. Averill M. Law, McgrawHill, 2006.
 Introduction to the Numerical Solution of Markov Chains. William J. Stewart, Princeton Univ. Pr., 1994.
 INTRODUCTION TO PROBABILITY. C. Grinstead and L. Snell
