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Entropy 2010, 12(5), 1145-1193; doi:10.3390/e12051145
Article

Entropy: The Markov Ordering Approach

1,* , 2 and 3
Received: 1 March 2010 / Revised: 30 April 2010 / Accepted: 4 May 2010 / Published: 7 May 2010
(This article belongs to the Special Issue Entropy in Model Reduction)
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Abstract

The focus of this article is on entropy and Markov processes. We study the properties of functionals which are invariant with respect to monotonic transformations and analyze two invariant “additivity” properties: (i) existence of a monotonic transformation which makes the functional additive with respect to the joining of independent systems and (ii) existence of a monotonic transformation which makes the functional additive with respect to the partitioning of the space of states. All Lyapunov functionals for Markov chains which have properties (i) and (ii) are derived. We describe the most general ordering of the distribution space, with respect to which all continuous-time Markov processes are monotonic (the Markov order). The solution differs significantly from the ordering given by the inequality of entropy growth. For inference, this approach results in a convex compact set of conditionally “most random” distributions.
Keywords: Markov process; Lyapunov function; entropy functionals; attainable region; MaxEnt; inference Markov process; Lyapunov function; entropy functionals; attainable region; MaxEnt; inference
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Gorban, A.N.; Gorban, P.A.; Judge, G. Entropy: The Markov Ordering Approach. Entropy 2010, 12, 1145-1193.

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