Student, who passed the course satisfactorily will be able to:

- understand basic notion of random variables and processes
- use Monte Carlo tecniques to calculate high dimensional integrals and expectations of random variables
- calculate posterior densities given the observations and a prior for quantifying the uncertain parameters using Bayesian techniques
- learn uncertainty propogation in models
- extend their knowledge in spectral methods to stochastic ones, such as, Karhunen-Loève Expansion, (generalised) Polynomial Chaos Expansion (gPC); Stochastic Galerkin Methods, Collocation, and Discrete Projection