The course objective is training students on abstract thinking and acquiring basic knowledge in the field of probability and stochastic processes. The aim is for students to master the basic concepts of probability theory, to enable them to choose the appropriate methods and to interpret the obtained results. The main goal is to enable students to apply theoretical knowledge from the theory of random processes in the field of information engineering.

Student should use the acquired knowledge in further education. The student is qualified to make and solve mathematical models in the further education in professional subjects applying acquired knowledge from probability and stochastic processes.

Basic definitions in probability, conditional probability and Bayes’ formula. Random variable of continuous and discrete type, distribution function. Two-dimensional random variable. Conditional distribution. Numerical properties – expectation, dispersion, covariance, correlation. Random processes – general terms. Markov chains and processes, the processes of birth and death, mass servicing systems.

Lectures; Numerical calculation practice. Consultations. Lectures are combined. In lectures, theoretical part of the course is taught followed by typical examples for better understanding. In practice, which accompanies lectures, typical problems are solved and knowledge from the lectures is deepened. Besides lectures and practice, consultations are held on a regular basis. Part of the course, presenting a logical whole, can be passed during the teaching process in the form of the following 3 modules (the first module: theory of probability, the second module: random variable, the third module: random processes). Oral part of the examination is optional.