Davide Gerosa — davide.gerosa@unimib.it
University of Milano-Bicocca, 2026.
Extracting knowledge from data—the numbers we measure in physics—requires rigorous statistical inference. This mini-course introduces the fundamentals of Bayesian reasoning and its role in modern scientific analysis. Participants will explore key stochastic sampling methods, including Markov Chain Monte Carlo and, time permitting, nested sampling. The session concludes with a hands-on astrophysics example, guiding students through a complete inference workflow in practice.
I prepared this material for a mini-course delivered at Instituto Superior Técnico, Lisbon, in February 2026. This is a small subset of what I usually teach in my class Astrostatistics and Machine Learning at the University of Milano-Bicocca; so have a look there for more stats.
Credit: xkcd 1236