Kengo Kamatani (Osaka University): Local consistency of Markov chain Monte Carlo with some applications

In this poster, we study the notion of efficiency (consistency)  and examine some asymptotic properties of Markov chain Monte Carlo  methods. We apply these results to some data augmentation procedures for independent and identically distributed observations.
The advantages of using the local properties are the simplicity and the generality of the results. The local properties provide useful insight into the problem of how to construct efficient algorithms.

Keywords; Asymptotic Normality, Ergodicity, Data augmentation

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