Probabilistic Models and Data Analysis

10. Apr, 2019
Type lecture
Semester Summer 2019
Schedule Mon & Wed, 10:15 – 11:45
Location Building E2 1, Rooms 001 & 007
Start 10. April 2019
End 30. September 2019
Language English
CMS

CMS

Please register in the CMS for the lecture. There you can find course materials, news, and more.

Schedule

  • Monday 10:15 – 11:45, Building E2 1, Room 001
  • Wednesday 10:15 – 11:45, Building E2 1, Room 007

Objective

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 Python to analyze data and probabilistic models. This course is a flipped classroom course, which means that the two lecture/tutorial slots will be used for exercises/examples/questions and discussion of the material; participants have to prepare for these slots by either watching videos or reading the lecture notes.

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. For the following topics, we have material (notes, videos) for revision:

  • Events and their probabilities
  • Rules of probability
  • Combinatorics
  • Conditional probability and independence
  • Distribution of a random variable
  • Expectation and variance

Syllabus

  • Short review of probability and random variables
  • Computer simulations and stochastic processes
    • Simulation of random variables
    • Monte Carlo methods
    • Markov chains
    • Hidden Markov models
  • Introduction to statistics and statistical inference
    • Simple descriptive and graphical statistics
    • Parameter estimation: Maximum Likelihood, Bayesian Inference, …
    • Confidence intervals
    • Hypothesis testing
    • Multiple Testing

Text Books

  • Probability and Statistics for Computer Scientists, Michael Baron, Taylor & Francis, 2013
  • Simulation Modelling and Analysis, Averill M. Law, McGraw-Hill, 2006
  • Introduction to the Numerical Solution of Markov Chains, William J. Stewart, Princeton University Press, 1994
  • Introduction to Probability, C. Grinstead and L. Snell (download)