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Convergence of the Markov chain Monte Carlo method
Dzurilla, Matúš ; Beneš, Viktor (advisor) ; Dostál, Petr (referee)
This thesis deals with the problem of random q-coloring from graph theory, in which goal is to color all vertices of graph by q colors so that no adjacent vertices have the same color. The aim is to generate random q-coloring from uniform distribution on the set of relevant solutions. The problem was expres- sed through Markov chain and approach was done through Markov Chain Monte Carlo method, namely the Gibbs sampler. The aim was to modify theorem of fast convergence of Gibbs sampler from systematic sweep to random sweep. It was ne- cessary to prove several auxiliary theorems, and in the proof of main theorem the "coupling" method was used. We managed to estimate the number of iterations needed to make the distance, in terms of total variation,from the distribution on the chain to the target distribution sufficiently small. The meaning og the the- orem was demonstrated in numerical examples and example od simulation was also added. 24
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Edgeworth expansion
Dzurilla, Matúš ; Omelka, Marek (advisor) ; Nagy, Stanislav (referee)
This thesis is focused around Edgeworth's expansion for approximation of distribution for parameter estimation. Aim of the thesis is to introduce term Edgeworth's expansion, its assumptions and terminology associated with it. Afterwards demonstrate process of deducting first term of Edgeworth's expansion. In the end demonstrate this deduction on examples and compare it with different approximations (mainly central limit theorem), and show strong and weak points of Edgeworth's expansion.
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Edgeworth expansion
Dzurilla, Matúš ; Omelka, Marek (advisor) ; Nagy, Stanislav (referee)
This thesis is focused around Edgeworths expansion for aproximation of distribution for parameter estimation. Aim of the thesis is to introduce term Edgeworths expansion, its assumptions and terminology associeted with it. Afterwords demonstrate process of deducting first term of Edgeworths expansion. In the end demonstrate this deduction on examples and compare it with different approximations (mainly central limit theorem), and show strong and weak points of Edgeworths expansion.
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Convergence of the Markov chain Monte Carlo method
Dzurilla, Matúš ; Beneš, Viktor (advisor) ; Dostál, Petr (referee)
This thesis deals with the problem of random q-coloring from graph theory, in which goal is to color all vertices of graph by q colors so that no adjacent vertices have the same color. The aim is to generate random q-coloring from uniform distribution on the set of relevant solutions. The problem was expres- sed through Markov chain and approach was done through Markov Chain Monte Carlo method, namely the Gibbs sampler. The aim was to modify theorem of fast convergence of Gibbs sampler from systematic sweep to random sweep. It was ne- cessary to prove several auxiliary theorems, and in the proof of main theorem the "coupling" method was used. We managed to estimate the number of iterations needed to make the distance, in terms of total variation,from the distribution on the chain to the target distribution sufficiently small. The meaning og the the- orem was demonstrated in numerical examples and example od simulation was also added. 24
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