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Bayesian inference for Markov chains

Part of: Limit theorems Inference from stochastic processes

Published online by Cambridge University Press:  14 July 2016

Peter Eichelsbacher  and
Ayalvadi Ganesh
Peter Eichelsbacher*
Affiliation:
Ruhr-Universität Bochum
Ayalvadi Ganesh*
Affiliation:
Microsoft Research
*
∗ Postal address: Fakultät für Mathematik, Ruhr-Universität Bochum, NA 3/68, D-44780 Bochum, Germany. Email address: peter.eichelsbacher@ruhr-uni-bochum.de
∗∗ Postal address: Microsoft Research, 7 J. J. Thomson Avenue, Cambridge CB3 0FB, UK.
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Abstract

We consider the estimation of Markov transition matrices by Bayes’ methods. We obtain large and moderate deviation principles for the sequence of Bayesian posterior distributions.

Keywords

Markov chainsBayesian inferencelarge deviationsmoderate deviations

MSC classification

Primary: 60F10: Large deviations
Secondary: 62M05: Markov processes

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 39 , Issue 1 , March 2002 , pp. 91 - 99
Copyright
Copyright © Applied Probability Trust 2002 

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Footnotes

Research supported by ARC Grant 880 from the Anglo-German foundation.

References

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