PSILOGO

Laboratory for Particle Physics (LTP)


LTP Colloquium

Probability, Uncertainty in Physics, and Bayesian Inference

Thursday, May 5, 2022, 16:00
online only                                             (for the zoom link contact michael.spira@psi.ch, johannes.schlenk@psi.ch or antonio.coutinho@psi.ch)

Giulio D'Agostini, University Sapienza Roma

Abstract:
Probabilistic reasoning is of greatest importance in tackling what Poincaré used to call "the essential problem of the experimental method", i.e. how to infer, from the observed effects, the causes that have produced them, all times when there is no deterministic link between causes (the parameters of our models of reality) and detectable consequences (the experimental observations). The approach outlined is basically that developed organically by Laplace, although it is presently known with the appellative `Bayesian'.

The revival, in the last few decades, of probabilistic reasoning in inference and forecasting is mainly due to unprecedentlty computing power (together with several mathematical progresses), which finally make possible to perform, although numerically or by Monte Carlo methods, the required calculations. Particular emphasis will be given to the conceptual and practical importance of the graphical representation of the inferential and predictive problems ("Bayesian networks").