Czech

Not applicable.

The student is able to:
• Describe the basic properties of random processes.
• Explain the basic Markov property.
• Build an matrix of a Markov chain.
• Explain the procedure to calculate the square matrix.
• Perform the classification of states of Markov chains in discrete and continuous case.
• Analyze a Markov chain using the Z-transform in the discrete case and the Laplace transformation in the continuous case.
• Explain the procedure for solving decision problems.
• Describe the procedure for solving the decision-making role with alternatives.
• Discuss the differences between the Markov chain and hidden Markov chain.

We require knowledge at the level of bachelor's degree, i.e. students must have proficiency in working with sets (intersection, union, complement), be able to work with matrices, handle the calculation of solving systems of linear algebraic equations using the elimination method and calculation of the matrix inverse, know the series and their sums, know the graphs of elementary functions and methods of construction, differentiate and integrate of basic functions.

Not applicable.

Techning methods include lectures and computer laboratories.

Requirements for successful completion of the course are provided in annual public notice. Students can obtain:
Up to 30 points for computer exercises that can be obtained a written test (20 points) and 10 points for activity assessment exercises.
Up to 70 points for the written final exam. The test contains both theoretical and numerical tasks that are used to verify the orientation in the problems of stochastic processes and their applications.
.

1. The construction of the necessary mathematical tools.
2. Probability.
3. Random processes, basic concepts, characteristics of random processes.
4. Discrete Markov chain. Homogeneous Markov chains, classification of states.
5. Regular Markov chains, limit vector, the fundamental matrix, and the median of the first transition.
6. Absorption chain mean transit time, transit and residence.
7. Analysis of Markov chains using Z-transform.
8. Calculation of powers of the transition matrix.
9. Continuous time Markov chains. Classification using the Laplace transform.
10. Poisson process. Linear growth process, linear process of extinction, linear process of growth and decline.
11. Markov decision processes. The award transitions. Asymptotic properties.
12. Decision-making processes with alternatives.
13. Hidden Markov processes.

Not applicable.

The aim of the course is to provide students with a comprehensive overview of the basic concepts and results relating to the theory of stochastic processes and especially Markov chains and processes. We show possibilities of application of the decision-making processes of various types.

Computer exercises are compulsory. Properly excused absence can be replaced by individual homework, which focuses on the issues discussed during the missed exercise.
Specifications of the controlled activities and ways of implementation are provided in annual public notice.
Date of the written test is announced in agreement with the students at least one week in advance. The new term for properly excused students is usually during the credit week.

Not applicable.

Not applicable.

BAŠTINEC, J.; SVOBODA, Z. Náhodné procesy. Brno: 2011. s. 1-182. (CS)

Not applicable.