Astrostatistics

Anno accademico 2022/2023
Docente Andrei Albert Mesinger

Didattica integrativa

Esercitazioni

Modalità d'esame

Prova scritta e relazione di seminario

Prerequisiti

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

Programma del corso

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.

Riferimenti bibliografici

materials will be provided during the course