Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once

In this seminar, current topics of applied statistics for bioinformatics data analysis will be discussed. Furthermore, we will also discuss classical concepts of stochastics/statistics and discrete mathematics. These will include, among others, the following: introduction to randomness, elementary and advanced combinatorics, random variables, discrete probability distributions and where they come from, conditional probabilities, Bayes? Theorem, continuous probability distributions, posterior distributions, descriptive statistics, moments of random variables (expectation, variance, ?), parametric models, statistical testing (frequentist view), statistical testing (Bayesian view), parameter estimation: maximum likelihood, parameter estimation in mixture models: EM algorithm, regression (simple linear, logistic, robust, multiple), robust regression, multiple regression, logistic regression. All concepts will be discussed in close relation to to current research in biomedice.

The students can independently work with supervision on a challenging topic.
Students gain experience in giving a technical presentation and producing a technical writeup in bioinformatics and in teaching. They can summarize, assess, classify, scientifically correctly represent and present concepts and methods of applied statistics in bioinformatics.
On the one hand, the students will get an overview of modern knowledge in the field of applied statistics in biology and medicine. On the other hand, they will know that there are still many open research questions in this field. By studying current articles and classical concepts, alike, the students have not only improved their reading and learning skills, but also their capability for translational thinking.

Bücher und Forschungsartikel / Books and research articles