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Entropy 2009, 11(4), 867-887; doi:10.3390/e11040867

Maximum Entropy Estimation of Transition Probabilities of Reversible Markov Chains

Queen Mary University of London, School of Mathematical Sciences, Mile End Road, London E1 4NS, UK
Received: 21 September 2009 / Accepted: 10 November 2009 / Published: 17 November 2009
(This article belongs to the Special Issue Maximum Entropy)
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Abstract

In this paper, we develop a general theory for the estimation of the transition probabilities of reversible Markov chains using the maximum entropy principle. A broad range of physical models can be studied within this approach. We use one-dimensional classical spin systems to illustrate the theoretical ideas. The examples studied in this paper are: the Ising model, the Potts model and the Blume-Emery-Griffiths model. View Full-Text
Keywords: maximum entropy principle; Markov chain; parameter estimation; statistical mechanics; spin chain models; thermodynamics maximum entropy principle; Markov chain; parameter estimation; statistical mechanics; spin chain models; thermodynamics
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Van der Straeten, E. Maximum Entropy Estimation of Transition Probabilities of Reversible Markov Chains. Entropy 2009, 11, 867-887.

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