Elements of probability theory: basic definitions, axiomatic theory of probability, cumulative distribution function and probability density function, definition of random variable (continuous and discrete), fractiles of a random variable, mean and variance, moments of a random variable, characteristic function, cumulants, synthetic indices of a variable random (mean, mode and median), Gaussian random variables, random variables with uniform density.
Analytical models for random phenomena: normal, log-normal, binomial, geometric distribution, Poisson process and distribution, exponential distribution, gamma, hypergeometric
Two-dimensional and multi-dimensional random variables: definition, marginal distribution, conditioned, independent and correlated. Examples.
Exercises: examples and applications on simple flat structural models. The exercises will be carried out using the Matlab programming environment. For this reason, the first exercises will be dedicated to the introduction into Matlab: work environment, operators, scientific calculation, script and function, graphics, constructs (if-then-else, for, while, switch cycle).