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Occupation measures for Markov chains

Published online by Cambridge University Press:  01 July 2016

J. W. Pitman*
Affiliation:
University of California, Berkeley

Abstract

An occupation measure describes the expected amount of time a stochastic process spends in different parts of its state space prior to a given random time. It is shown that a basic identity involving occupation measures provides a unified approach to a variety of moment identities for Markov chains, and some connections with potential theory are made.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1977 

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References

[1] Blumenthal, R. M. and Getoor, R. K. (1968) Markov Processes and Potential Theory. Academic Press, New York and London.Google Scholar
[2] Chung, K. L. (1967) Markov Chains with Stationary Transition Probabilities, 2nd edn. Springer, Berlin.Google Scholar
[3] Derman, C. (1954) A solution to a set of fundamental equations in Markov chains. Proc. Amer. Math. Soc. 5, 332334.Google Scholar
[4] Dynkin, E. B. and Yushkevitch, A. A. (1969) Markov Processes; Theorems and Problems. Plenum Press, New York.CrossRefGoogle Scholar
[5] Feller, W. (1971) An Introduction to Probability Theory and its Applications, Vol. 2, 2nd edn. Wiley, New York.Google Scholar
[6] Freedman, D. (1971) Markov Chains. Holden Day, San Francisco.Google Scholar
[7] Itô, K. and McKean, H. P. (1965) Diffusion Processes and their Sample Paths. Springer, Berlin.Google Scholar
[8] Kemeny, J. G. and Snell, J. L. (1961) Potentials for denumerable Markov chains. J. Math. Anal. Appl. 3, 196260.CrossRefGoogle Scholar
[9] Kemeny, J. G. and Snell, J. L. (1961) Finite continuous time Markov chains. Theor. Prob. Appl. 6, 110115.Google Scholar
[10] Kemeny, J. G., Snell, J. L. and Knapp, A. W. (1966) Denumerable Markov Chains. Van Nostrand, Princeton.Google Scholar
[11] Lamperti, J. (1960) The first passage moments and the invariant measure of a Markov chain. Ann. Math. Statist. 31, 515517.Google Scholar
[12] Meyer, P. A. (1966) Probability and Potentials. Blaisdell, Waltham, Mass. Google Scholar
[13] Neveu, J. (1964) Chaînes de Markov et théorie du potentiel. Ann. Fac. Sci. Clermond-Ferrand 3, 3765.Google Scholar
[14] Neveu, J. (1970) Chaînes de Markov. Textos de Matematica, no. 19, Instituto de Matematica, Univ. Federal de Pernambuco.Google Scholar
[15] Pitman, J. W. (1974) An identity for stopping times of a Markov process. In Studies in Probability and Statistics, ed. Williams, E. J.. Jerusalem Academic Press.Google Scholar
[16] Pitman, J. W. (1974) Uniform rates of convergence for Markov chain transition probabilities. Z. Wahrscheinlichkeitsth. 29, 193227.Google Scholar
[17] Riordan, J. (1958) An Introduction to Combinatorial Analysis. Wiley, New York.Google Scholar
[18] Walsh, J. and Weil, M. (1972) Representation de temps terminaux et applications aux fonctionelles additives et aux systèmes de Lévy. Ann. Sci. École Norm. Sup. (4) 5, 121155.Google Scholar