Entry requirements

Mathematical statistics.

Aims

The course aims at deepening the knowledge of probability theory and stochastic processes, and to prepare and give students some experience of modelling real phenomena using stochastic models.

Content

The Poisson process, lifelength analysis. Risk and error. Markov chains and birth-and-death models. Stationary processes, covariance calculations, Yule-Walker equations. Introduction to time-series analysis. Introduction to Brownian motion.

Applications in computer communications and population dynamics. Aspects of simulation and statistical inference for stochastic systems.

Instruction

Lectures.

Assessment

Written test at the end of the course. Mandatory

assignments can be used during the course.