Ability of abstract thinking and acquiring basic knowledge in the area of probability, statistics and stochastic processes.

In their further education and professional subjects students are competent to develop and solve mathematical models in the area of probability, statistics and stochastic processes.

Basic definitions in probability, conditional probability and Bayes’ formula. Random variable of discrete and continuous type, distribution functions. Two dimensional random variable. Conditional distribution. Numeric characteristics – expectation, dispersion, covariance, correlation. Conditional expectation. Limit theorems. Statistics – point estimate and interval estimate, parametric and nonparametric hypotheses and significance testing. Stochastic processes – general notions. Stochastic process transformation- derivative, integral. Poisson process, white noise, telegraph signal. Markov chains and processes, birth-death process, mass service systems. Stationary process. Mass service systems.

Lectures, Numerical calculation practice and computer practice (statistics). Consultations. Lectures are conducted combining theoretical part of the subject matter with characteristic examples which facilitate understanding. During practice classes, which accompany the lectures, some characteristic problem tasks are done and the presented material is discussed in more detail. In addition to the lecture and practice classes there are regular consultations. Parts of the course which form a logical unit can be taken during the course in the form of 4 partial exams based on the modules (module one: probability theory, module two: random variable, module three: statistics, module four: stochastic processes). The oral part of the final exam is not obligatory.