LFU:online

At the end of this course the students should:

1)       be able to autonomously install and use Python, as well as Python notebooks and Python packages,

2)       be familiar with basic Python packages for data visualisation (matplotlib),

3)       be familiar with simple numerics packages (numpy, scipy) and know how to solve integration problems with them, and

4)       have understood the basics of Bayesian inference and be able to perform such inferences.

This course will start by introducing students to the programming language Python, reviewing in a mix of theory and hands on coding how to solve linear equation systems, perform Fourier analyses and numerical integration, and employ Monte Carlo algorithms. We will then discuss in the same format Bayesian inference and explore the different tools used for it, covering likelihood maximisation, posterior sampling, Bayesian model comparison and Hamiltonian Monte Carlo sampling.

Lectures and tutorials including coding / scripting on personal computer

Course examination according to § 7, statute section on "study-law regulations"

Physics undergraduate mathematics

Basic programming skills: Will be learned during the course

Helpful, but not mandatory: Statistics