Period of duration of course
Course info
Number of course hours
Number of hours of lecturers of reference
Number of hours of supplementary teaching

Type of exam

Written exam and seminars


PhD students, as well as 4th and 5th year undergraduates


This course deals with statistical data analysis, with an application towards astronomical and cosmological observations.  Starting from Bayes' theorem, we show how it can be used for data-driven inference, including analytical, Monte Carlo, as well as machine learning implementations.  Topics to be covered include: introduction to probability, maxlimum likelihood estimation, Bayesian vs classical frequentist statistics, parameter inference (analytic and Monte Carlo methods), model selection, likelihood free inference, data compression, introduction to neural networks and their applications in inference.  Examples will be given throughout, with a focus towards topical problems in astrophysics and cosmology.

Educational aims

The students should come away with an understanding of Bayesian inference, including some practical applications.

Bibliographical references

materials will be provided during the course