Introduction to the probability theory; distribution functions: representation and features; extreme event statistics; elements of parametric and non-parametric statistical analysis, information theory and correlation; practical exercises.
Co-teaching: Prof. Berrilli Francesco
The aim of the course is to provide the basic knowledge of the methods for the non-parametric and parametric statistical analysis of large dataset. In detail, the course is devoted to the description of different methods and tests for the comparison of statistical properties of different large dataset, and also for the study of the existence of a linear and nonlinear correlation among datasets using also approaches based on information theory.
KNOWLEDGE AND UNDERSTANDING:
basic principles and concepts of the advanced statistics and information theory and its implementation on data sets
APPLYING KNOWLEDGE AND UNDERSTANDING:
to apply the basic principles and concepts of advanced statistics and information theory to get a quantitative description of the observed phenomena and of the nature of the correlation among sets of physical data.
capacity to extract independently the fundamental information on physical systems through the statistical analysis and the information theory, and to be capable of discerning the relevance of the works in the specific field.
to the student is required to be able to explain the nature of the statistical relations and analysis and the inference and correlation among different datasets of physical systems to both a specialized and not-specialized audience
capacity to unterstand the importance of the different elements determining the dynamics of physical systems by the statistical analysis.