Bayesian Approach to Model Selection in Hidden Markov Models

Student: Shvets Pavel

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

Final Grade: 8

Year of Graduation: 2017

Hidden Markov Models play important role in time series analysis and sequence analysis. Nonparametric approaches have an opportunity to introduce new possibilities for applied tasks. In current work inference algorithm introduced for model with infinite number of hidden states as an alternative to Gibbs sampling and described its benefits caused by sampling whole sequence of hidden states.

Full text (added May 30, 2017)

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