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Queueing theory utilization in packet network design and optimization process
Rýzner, Zdeněk ; Zeman, Václav (referee) ; Novotný, Vít (advisor)
This master's thesis deals with queueing theory and its application in designing node models in packet-switched network. There are described general principles of designing queueing theory models and its mathematical background. Further simulator of packet delay in network was created. This application implements two described models - M/M/1 and M/G/1. Application can be used for simulating network nodes and obtaining basic network characteristics like packet delay or packet loss. Next, lab exercise was created, in that exercise students familiarize themselves with basic concepts of queueing theory and examine both analytical and simulation approach to solving queueing systems.
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Tennis match modelling using Markov chains
Walica, Roman ; Hübnerová, Zuzana (referee) ; Hrabec, Pavel (advisor)
This thesis deals with the application of Markov chains in the tennis field and their subsequent adjustment based on formulated hypotheses. The first part of thesis describes the principles of the tennis game. In the second part, we deal with concepts from the field of statistics. These concepts are mainly used for creation and subsequent branching of Markov chains. The result of this work is several Markov chains for the game, divided by tennis serve or reception or by the type of surface on which the match is played. The other chains mentioned are chains for tiebreak, set and match. In final part of thesis we introduce obtained prediction of the result and the duration of the tennis match and its modeled parts.
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Coupling and speed of convergence of discrete MCMC algorithms.
Kalaš, Martin ; Prokešová, Michaela (advisor) ; Dvořák, Jiří (referee)
Convergence of the marginal distribution of a Markov chain to its stationary distribution is an essential property of this model with many applications in different fields of modern mathematics. Such typical applications are for example the Markov Chain Monte Carlo algorithms, which are useful for sampling from complicated probability distributions. A crucial point for usefulness of such algorithms is the so called mixing time of corresponding Markov chain, i.e. the number of steps the chain has to make for the difference between its current marginal distribution and stationary distribution to be sufficiently small. The main goal of this thesis is to describe a method for estimation of the mixing time based on a probability technique called coupling. In the first part we collect some definitions and propositions to show how the method works. Later the method is demonstrated on several traditional examples of Markov chains including e.g. random walk on a graph. In the end we study Metropolis chain on the set of proper colorings of a graph as a specific example of MCMC algorithm and show how to estimate its mixing time.
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Mixing of Markov chains - lower bounds for mixing
Ditrich, Jakub ; Prokešová, Michaela (advisor) ; Swart, Jan (referee)
The focus of the thesis is the convergence of irreducible aperiodic homoge- neous Markov chains with a finite and discrete set of states. Specifically, lower bounds on the time needed for the chain's marginal probability distribution to be sufficiently close to the stationary distribution, so called mixing time. Multiple methods are introdu- ced, properly motivated and proven. Finally, each method is demonstrated on a suitable example. The result is an overview of three methods that can be used to derive lower bounds for mixing time. 1
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Runs and Randomness
Zdeněk, Pavel ; Čoupek, Petr (advisor) ; Antoch, Jaromír (referee)
In this thesis probability distribution of five random variables related to success runs in a sequence of Bernoulli trials was found. The techinque of imbedding random sequences into Markov chains is used and improved compared to existing results. For every run a Markov chain was constructed, the definiton of imbedding was verified, a method for computation of its distribution was stated and examples of distribution were computed. 1
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Markov binomial model
Šuléřová, Natálie ; Hudecová, Šárka (advisor) ; Dvořák, Jiří (referee)
In this thesis we study the Markov chain binomial model, which generalizes the standard binomial distribution. Instead of the sum of independent random vari- ables, we consider the sum of random variables that form a stationary Markov chain. The goal of this thesis is to describe this model along with its proper- ties, such as the expected value, variance and probability generating function. A part of this thesis is dedicated to estimating parameters of this model using the method of moments and the maximum likelihood estimation. The accuracy of the methods is compared in a simulation study and obtained results are dis- cussed. The presented model is then applied on a real dataset based on rate of alcohol-impaired car accidents.
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Multi - event Bonus - Malus System
Kaplanová, Martina ; Mazurová, Lucie (advisor) ; Branda, Martin (referee)
This work deals with bonus - malus systems for automobile insurance that distinguishtypes of claim. The first part of this work is definition of bonus - malus systems that do not distinguish types of claim and then their expansion just to multi - event bonus - malus systems. The main focus of the work is computation of stationary distribution for different systems, which means the distribution of classes in which the system stabilizes. Furthermore, there are several simulations of trajectory of insured through the system based on the number and type of accidents that they have caused. Finally, relative frequencies of classes in which insured is at the end of the simulation and the stationary distribution of the system are compared. Powered by TCPDF (www.tcpdf.org)
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Coupling and speed of convergence of discrete MCMC algorithms.
Kalaš, Martin ; Prokešová, Michaela (advisor) ; Dvořák, Jiří (referee)
Convergence of the marginal distribution of a Markov chain to its stationary distribution is an essential property of this model with many applications in different fields of modern mathematics. Such typical applications are for example the Markov Chain Monte Carlo algorithms, which are useful for sampling from complicated probability distributions. A crucial point for usefulness of such algorithms is the so called mixing time of corresponding Markov chain, i.e. the number of steps the chain has to make for the difference between its current marginal distribution and stationary distribution to be sufficiently small. The main goal of this thesis is to describe a method for estimation of the mixing time based on a probability technique called coupling. In the first part we collect some definitions and propositions to show how the method works. Later the method is demonstrated on several traditional examples of Markov chains including e.g. random walk on a graph. In the end we study Metropolis chain on the set of proper colorings of a graph as a specific example of MCMC algorithm and show how to estimate its mixing time.
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Queueing theory utilization in packet network design and optimization process
Rýzner, Zdeněk ; Zeman, Václav (referee) ; Novotný, Vít (advisor)
This master's thesis deals with queueing theory and its application in designing node models in packet-switched network. There are described general principles of designing queueing theory models and its mathematical background. Further simulator of packet delay in network was created. This application implements two described models - M/M/1 and M/G/1. Application can be used for simulating network nodes and obtaining basic network characteristics like packet delay or packet loss. Next, lab exercise was created, in that exercise students familiarize themselves with basic concepts of queueing theory and examine both analytical and simulation approach to solving queueing systems.
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