Bayesian Model Selection for Partially Observed Markov Process with Two States

Student: Posashkov Dmitrii

Educational Programme: Mathematical Methods of Optimization and Stochastics (Master)

Final Grade: 8

Year of Graduation: 2017

Model selection for partially observed Markov process with two States in this work is based on sampling Monte Carlo scheme of Markov chains. The task was to estimate the parameter of the distribution of the hidden Markov variable with two States, symmetric and sparse transitions. The paper formulates hypotheses which determine the choice of method for solving the problem. The analysis of the Monte Carlo Markov chains was carried out, and on its basis sampling according to the Gibbs pattern was produced. According to the obtained results the probability of rating transition probabilities was calculated in two ways: through the likelihood function and minimizing the functional of squared deviation of observation values generated DMC.

Full text (added May 30, 2017)

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