Comprehensive view of subjects in information theory that are central to problems in statistical inference, advanced probability and probabilistic modeling (statistical mechanics). Working knowledge of the basic concepts in information theory and their application to statistical learning and mathematical modeling.
Information Theory
Learning Goals
Program in pills
Asymptotic Equipartition Property and Shannon’s theorem. Entropy, mutual and relative information. Limit theorems for sums and extremes. Large deviation theory and distributions of maximal entropy. Elements of coding theory. Applications to statistical inference.
Area
Mathematical and statistical modelling
Curriculum Foundations
TAF Type
Curriculum Industry
TAF Type
Curriculum Health
TAF Type
Curriculum Economy
TAF Type
SSD
ECTS
Semester
Lecturers
Matteo Marsili