Welcome to our intensive one-week doctoral school on Bayesian Statistics, a program designed to bridge the gap between foundational theory and the cutting edge of modern inference. This is the second edition of a series of schools on the use of AI and modern computing in Physics, building upon the foundations laid during the previous edition.
Over the course of five days, participants will start from the core principles of Bayesian Data Analysis to the advanced frontiers of Bayesian Neural Networks and Simulation-Based Inference.
Common pitfalls in application will be analysed and a flagship case study in the detection and characterisation of gravitational waves will be studied.
The schedule integrates extensive hands-on sessions after each lecture.
The program also features a free-of-charge half-day visit to the underground facility of the Laboratori Nazionali del Gran Sasso .
The school is offered with no registration fee and includes coffee breaks and lunches. Travel, dinner and accommodation are to be covered by participants. 
Attendees are expected to check the previous knowledge page prior to attending the school.
Registration will close on June 30th. Please note that applicants must provide a short reference letter from their supervisor.
Lecturers

Eleni Tsaprazi (Paris Observatory)
Davide Valsecchi (ETH Zurich)
Alan Heavens (Imperial College London)
Filippo Santoliquido (Gran Sasso Science Institute)

 
Organising Committee

Martino Borsato (Unimib and INFN)
Pietro Govoni (Unimib and INFN)
Jan Harms (GSSI)
Ezio Previtali (Unimib and LNGS/INFN)
Tommaso Tabarelli de Fatis (Unimib and INFN)


https://agenda.infn.it/event/51770/