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Last update: T_KSI (15.04.2003)
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Last update: G_M (05.06.2008)
The students will acquaint with the basics of the Markov chains, birth and death processes, queueing models and stochastic processes. They will be able to undestand stochstic approach to the modelling of real random events in this domain.
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Last update: RNDr. Jitka Zichová, Dr. (10.05.2018)
Oral exam. |
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Last update: prof. RNDr. Jaromír Antoch, CSc. (05.10.2018)
Prášková Z. a P. Lachout, Základy náhodných procesů, Karolinum, Praha 1998.
Feller W., An introduction to probability theory and its applications, Wiley, New York 1970.
Ross, S.M. Introduction to Probability Models. Academic Press, Elsevier, 2007. |
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Last update: G_M (29.05.2008)
Lecture. |
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Last update: prof. RNDr. Jaromír Antoch, CSc. (30.11.2021)
Examination is oral within a framework of discussed matter given by the syllabus a scope presented during the lecture. TODO It is necessary to know all fundamental definitions, theorems and assertions (including the assumptions), understand their inter relations and be capable in outline explain their justification (proofs). Student should be able to analyze real problems.
Provided COVID situation will not allow oral exam, we will pass to a suitable distant form (on line), which will be specified according to the current epidemiologic situation.
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Last update: doc. RNDr. Petr Hnětynka, Ph.D. (07.02.2019)
• Discrete and continuous random variables and their characteristics. • Recurrent events, their classification and applications. • Markov chains with discrete states and discrete time, classification of states, stationary distribution, etc. • Exponential distribution, its properties and applications • Markov processes with discrete states and continuous time. • Models of birth and death. • Basics of theory of queues, modeling of serving networks. • Poisson process and its applications. • Durbin-Watson branching process and its applications • Simulation of random objects studied during the lecture |
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Last update: prof. RNDr. Jaromír Antoch, CSc. (04.06.2018)
Random variables and vectors and their characterizations; convergence in distribution and in probability; central limit theorem; conditional density and conditional expectation; linear differencial equations. |