As far as knowledge development, the goal is learning the fundamentals of probability and elements of descriptive statistics and parameter estimate, to be able to perform a basic analysis experimental data. In terms of skills, the target is the development of a "problem solving" attitude, where the focus is on data driven quantitative analysis.
Course Prerequisites
High School mathematics and first year calculus.
Teaching Methods
lectures & problem solving sessions. Lecture notes will be made available in pdf format, together with articles, lecture notes from other resources, exercise books, else.
Assessment Methods
Written exam, about 2-3 hour long, based on solving a series of problems. Oral exam, where the proposed solutions in the written exam will be discussed. This will be complemented by an in-depth discussion on a specific topic in accordance with the professor in charge. The topic will presume the background knowledge by the lectures to go beyond. The top mark (cum laude) presumes an excellent written examination and a brilliant analysis of the topics addressed in the oral discussion.
Contents
- deterministic, stochastic and "uncertain" observables - errors and their measurement - Combinatorial calculus & Elements of probability • Frequentist definition of probability • Conditional probability, correlation • Distributions, permutations and all that • Bayes’ theorem • Probability distribution function • Moment generating function - Descriptive statistics • Mean, median, moda • Variance and standard deviation • Weighted mean • Error propagation - Bernoulli, Poisson, Gauss probability distribution functions - Central limit theorem - Parameter estimate, optimal fit • Maximum likelihood • Chi squared mimimization
Course Language
Italian
More information
The professor in charge can be contacted via e-mail at massimo.caccia@uninsubria.it
