At the end of this course, the student will learn:

- the generation of pseudorandom numbers from a given distribution
- basics of Monte Carlo methods and variance reduction techniques
- the algorithms for numerical solutions of stochastic differential equations, such as Euler-Maruyama and Milstein schemes, and convergence of numerical methods
- the simulation of Levy processes, in particular, jump-diffusion processes by Euler-Maruyama method for jump-diffusions
- possible fields of applications of continuous-time stochastic processes with continuous and discontinuous paths
- basic principles of Markov chain Monte Carlo methods and Bayesian estimation