Intelligent Modeling and Simulation Lifecycle

Sun, 01 Jan 2023 00:00:00 +0000

Probabilistic Models and Data Analysis

Wed, 10 Apr 2019 00:00:00 +0000

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)

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Intelligent Modeling and Simulation Lifecycle

Probabilistic Models and Data Analysis

CMS

Schedule

Objective

Prerequisites

Syllabus

Text Books

Modeling and Simulation

Rule-Based and Hybrid Simulation of Reaction Networks

Modeling and Simulation

Ringvorlesung Bioinformatik: Stochastische Simulation biochemischer Reaktionen

Modeling and Simulation

Modeling and Simulation