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Monte Carlo Potts model
 Vlachovský, Karel ; Beneš, Viktor (advisor) ; Dvořák, Jiří (referee)
Potts model is a generalisation of the Ising model which is used in statistical mechanics. Our goal is to sample from the distribution of that model. However, the state space is too large, so we cannot sample from it directly. We will use Markov Chain Monte Carlo methods instead. It means that the markov chain would have Potts distribution as its stationary distribution. We will compare Gibbs sampler, Metropolis algorithm, Swendsen-Wang algorithm and significantly we will introduce a new mixing algorithm. We will show that all these algorithms are uniformly ergodic. We will implement them and show that it is wise to use only mixing algorithm and Swendsen-Wang algorithm for larger parameter of temperature for Potts model. 1
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