Convergence Analysis of MCMC Methods

Student: Luchkin Vadim

Educational Programme: Mathematics (Bachelor)

Year of Graduation: 2020

In this work we consider the problem of sampling from probability distribution based on observed data and known up to a normalization constant. Since some of the cases can not be sampled in explicit way, we use Markov Chain Monte Carlo methods. These methods can be computationally expensive, therefore, it is important to evaluate their running time. Here we focus on theoretical study of Split Gibbs Sampler algorithm. We introduce an expansion of this sampling algorithm that can be used on a wider class ofdistributions than Split Gibbs Sampler.

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