LFU:online

Understand key concepts and techniques in Bayesian statistical inference. Apply Bayesian statistical inference techniques to tackle typical problems in astronomy such as source detection, catalogues and selection effects, correlation testing, source counts and luminosity functions.

This course will focus on Bayesian statistical inference and applications in astronomy. Starting from deriving Bayes’ theorem based on Cox’s rules for consistent reasoning, we will follow the conceptual steps of Bayesian data analysis which include the formulation of the data likelihood, the choice of the prior, determination of the posterior, marginalization of nuisance parameters, parameter estimation (single and multiple variables), quantification of uncertainty in parameter estimates and propagation of uncertainties, hypothesis testing and model selection. Example applications (especially in astronomy) will also be provided and discussed in detail.

The course will be held as block from Jan 15^th –Jan 19^th. Students are required to attend the lecture, do homework and submit a final project two weeks after the end of the course (that will count towards their final grade)

Blockveranstaltung 15.Jan.-19.Jan2018