On the entropy for semi-Markov processes



Journal of Applied Probability

On the entropy for semi-Markov processes

Valerie Girardin and Nikolaos Limnios

Source: J. Appl. Probab. Volume 40, Number 4 (2003), 1060-1068.

Abstract

The aim of this paper is to define the entropy of a finite semi-Markov process. We define the entropy of the finite distributions of the process, and obtain explicitly its entropy rate by extending the Shannon-McMillan-Breiman theorem to this class of nonstationary continuous-time processes. The particular cases of pure jump Markov processes and renewal processes are considered. The relative entropy rate between two semi-Markov processes is also defined.

Primary Subjects: 60K15, 60J25, 62B10, 94A17
Keywords: Relative entropy; entropy rate; semi-Markov process; pure jump Markov process

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber.
If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text
Alternatively, the document is available for a cost of $6. Select the "buy article" button below to purchase this document from a secured VeriSign, Inc. site.
Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.jap/1067436100
Digital Object Identifier: doi:10.1239/jap/1067436100
Mathematical Reviews number (MathSciNet): MR2012686
Zentralblatt MATH identifier: 02103422

References

Albert, A. (1962). Estimating the infinitesimal generator of a continuous time finite state Markov process. Ann. Math. Statist. 38, 727--753.
Mathematical Reviews (MathSciNet): MR137159
Bad Dumitrescu, M. (1988). Some informational properties of Markov pure-jump processes. Cas. Pestovani Mat. 113, 429--434.
Mathematical Reviews (MathSciNet): MR981884
Breiman, L. (1958). The individual ergodic theorem of information theory. Ann. Math. Statist. 28, 809--811.
Mathematical Reviews (MathSciNet): MR92710
Breiman, L. (1960). Correction to: the individual ergodic theorem of information theory. Ann. Math. Statist. 31, 809--810.
Mathematical Reviews (MathSciNet): MR92710
Gut, A. (1988). Stopped Random Walks, Limit Theorems and Applications. Springer, New York.
Mathematical Reviews (MathSciNet): MR916870
Limnios, N. and Opri\csan, G. (2001). Semi-Markov Processes and Reliability. Birkhauser, Boston, MA.
Mathematical Reviews (MathSciNet): MR1843923
Mcmillan, M. (1953). The basic theorems of information theory. Ann. Math. Statist. 24, 196--219.
Mathematical Reviews (MathSciNet): MR55621
Moore, E. H. and Pyke, R. (1968). Estimation of the transition distributions of a Markov renewal process. Ann. Inst. Statist. Math. 20, 411--424.
Mathematical Reviews (MathSciNet): MR240934
Perez, A. (1964). Extensions of Shannon--McMillan's limit theorem to more general stochastic processes. In Trans. Third Prague Conf. Inf. Theory, Statist. Decision Functions, Random Processes, Publishing House of the Czechoslovak Academy of Science, Prague, pp. 545--574.
Mathematical Reviews (MathSciNet): MR165996
Pinsker, M. S. (1964). Information and Information Stability of Random Variables and Processes. Holden-Day, San Francisco, CA.
Mathematical Reviews (MathSciNet): MR213190
Shannon, C. E. (1948). A mathematical theory of communication. Bell Syst. Tech. J. 27, 379--423, 623--656.
Mathematical Reviews (MathSciNet): MR26286

2008 © Applied Probability Trust