Main field(s) of study and in-depth level:
Fail (U), 3, 4, 5.
The Faculty Board of Science and Technology
Alt. 1) 120 credits in the engineering programme in molecular biotechnology including Multivariate data analysis and experimental design. Alt. 2) 120 credits. Bioinformatics - starting course (can be read in parallel).
After passing the course the student should be able to
account for and apply classical statistical inference based on Bayesian and frequentist methods, traditional computer-based methods, as well as computer-intensive methods, for analysis one variable alone and several variables at the same time
choose and apply the appropriate among above-mentioned methods and technologies for statistical inference for a given set of biological and biomedical molecular data and their associated biomedical questions.
Classical inference: Frequentist and Bayesian inference, maximum likelihood estimation. Traditional computer-based methods: Empirical Bayes, ridge regression, generalized linear models, regression trees, survival analysis and the EM-algorithm. Computer-intensive methods as resampling, resampling based confidence intervals, cross validation, large-scale hypothesis testing, sparse regression models, random forests, and boosting. Bioinformatic application examples.
Lectures, calculation exercises and computer exercises.
Written examination (3 credits), computer exercises (2 credits).
week 27, 2017
Computer age statistical inference : algorithms, evidence, and data science