Embedded Files
Probabilistic Inference and Forecasting in Science is one of the so called general-topic course offered to the PhD students in Physics at Sapienza Università di Roma. The main topic of the course is to discuss how to qualify uncertainties associated to measurements by means of probability theory. More in general, such an approach applies to all the fields where statements are supported by a limited amount of information.
Probabilistic Inference and Forecasting in Science is one of the so called general-topic course offered to the PhD students in Physics at Sapienza Università di Roma. The main topic of the course is to discuss how to qualify uncertainties associated to measurements by means of probability theory. More in general, such an approach applies to all the fields where statements are supported by a limited amount of information.
The course starts by reviewing the basic concepts of probability theory. With the help of bayesian networks, real life problems are represented by a probabilistic model, and, based on the available informaton, solved for the quantity of interest. This inferential method is put in connection with the process of mesuring a physics quantity and quantifying the associated uncertainty. A similar approach is used to rank the strength of the cause-effect link between hypotheses and observations depending on the specific results of experiments. Particular emphasis is given to the discussion of several topics of practical interests, including counting experiments, upper/lower bounds, systematic errors, combination of measurements, model fits, paramerer estimation, and comparison of hypotheses.
The course starts by reviewing the basic concepts of probability theory. With the help of bayesian networks, real life problems are represented by a probabilistic model, and, based on the available informaton, solved for the quantity of interest. This inferential method is put in connection with the process of mesuring a physics quantity and quantifying the associated uncertainty. A similar approach is used to rank the strength of the cause-effect link between hypotheses and observations depending on the specific results of experiments. Particular emphasis is given to the discussion of several topics of practical interests, including counting experiments, upper/lower bounds, systematic errors, combination of measurements, model fits, paramerer estimation, and comparison of hypotheses.
Dipartimento di Fisica - Sapienza Università di Roma. giulio.dagostini@roma1.infn.it
He is an experimental particle physicist, who has collaborated in large fronter-type experiments, working on various aspects of the construction and the operation of detectors, and analysing the resulting data. He has also performed several re-analyses of data produced by other experiments. Physics topics to which he has contributed include: study of forces between quarks and gluons; quark fragmentation, heavy quark decay; proton and photon structure functions; new particle searches (dibaryons, excited quarks, supersymmetric particles, electron compositeness, Higgs particle).
Prof. Stefano Piacentini
Prof. Stefano Piacentini
Facoltà di Fisica - Gran Sasso Science Institute (L'Aquila) stefano.piacentini@gssi.it
He is an experimental particle physicist and his main interest is the direct detection of light dark matter particles. He has always been interested on the applications of inferential methods to rare events physics. He is contributing to the development of two experiments in the field of direct detection of dark matter: DarkSide and CYGNO.
Prof. Andrea Messina
Prof. Andrea Messina
Dipartimento di Fisica - Sapienza Università di Roma. andrea.messina@uniroma1.it
He is an experimental particle physicist. His research focuses on dark matter direct detection and on collider physics. He teaches Laboratory of Mechanics for the undergraduate programme in Physics. Since 2019 he has been teaching the course Uncerainty and Probability for the PhD programme in Physics.
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma- www.uniroma1.it
Page updated
Report abuse