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On the total reward variance for continuous-time Markov reward chains

Published online by Cambridge University Press:  14 July 2016

Nico M. Van Dijk*
Affiliation:
University of Amsterdam
Karel Sladký*
Affiliation:
Institute of Information Theory and Automation, Prague
*
Postal address: Department of Economic Sciences and Econometrics, University of Amsterdam, Roetersstraat 11, 1018 WB Amsterdam, The Netherlands. Email address: nivd@fee.uva.nl
∗∗Postal address: Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, PO Box 18, Pod Vodárenskou věží 4, 182 08 Prague 8, Czech Republic. Email address: sladky@utia.cas.cz
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Abstract

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As an extension of the discrete-time case, this note investigates the variance of the total cumulative reward for continuous-time Markov reward chains with finite state spaces. The results correspond to discrete-time results. In particular, the variance growth rate is shown to be asymptotically linear in time. Expressions are provided to compute this growth rate.

Type
Research Papers
Copyright
© Applied Probability Trust 2006 

References

Benito, F. (1982). Calculating the variance in Markov-processes with random reward. Trabajos Estadı´st. Investigación Operat. 33, 7385.CrossRefGoogle Scholar
Dynkin, E. B. (1965). Markov Processes, Vol. I. Springer, Berlin.Google Scholar
Filar, J., Kallenberg, L. C. M. and Lee, H.-M. (1989). Variance penalized Markov decision processes. Math. Operat. Res. 14, 147161.CrossRefGoogle Scholar
Huang, Y. and Kallenberg, L. C. M. (1994). On finding optimal policies for Markov decision chains: a unifying framework for mean-variance-tradeoffs. Math. Operat. Res. 19, 434448.Google Scholar
Jaquette, S. C. (1972). Markov decision processes with a new optimality criterion: small interest rates. Ann. Math. Statist. 43, 18941901.CrossRefGoogle Scholar
Jaquette, S. C. (1973). Markov decision processes with a new optimality criterion: discrete time. Ann. Statist. 1, 496505.CrossRefGoogle Scholar
Jaquette, S. C. (1975). Markov decision processes with a new optimality criterion: continuous time. Ann. Statist. 3, 547553.Google Scholar
Jaquette, S. C. (1976). A utility criterion for Markov decision processes. Manag. Sci. 23, 4349.CrossRefGoogle Scholar
Kadota, Y. (1997). A minimum average-variance in Markov decision processes. Bull. Inf. Cybernet. 29, 8389.CrossRefGoogle Scholar
Kawai, H. (1987). A variance minimization problem for a Markov decision process. Europ. J. Operat. Res. 31, 140145.Google Scholar
Kurano, M. (1987). Markov decision processes with a minimum-variance criterion. J. Math. Anal. Appl. 123, 572583.CrossRefGoogle Scholar
Mandl, P. (1971). On the variance in controlled Markov chains. Kybernetika 7, 112.Google Scholar
Odoni, R. A. (1969). On finding the maximal gain for Markov decision processes. Operat. Res. 17, 857860.Google Scholar
Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley, New York.CrossRefGoogle Scholar
Ross, S. M. (1970). Applied Probability Models with Optimization Applications. Holden-Day, San Francisco, CA.Google Scholar
Sladký, K. and Sitař, M. (2004). Optimal solutions for undiscounted variance penalized Markov decision chains. In Dynamic Stochastic Optimization (Lecture Notes Econom. Math. Systems 532), eds Marti, K., Ermoliev, Y. and Pflug, G., Springer, Berlin, pp. 4366.Google Scholar
Sobel, M. J. (1982). The variance of discounted Markov decision processes. J. Appl. Prob. 19, 794802.CrossRefGoogle Scholar
Sobel, M. J. (1985). Maximal mean/standard deviation ratio in an undiscounted MDP. Operat. Res. Lett. 4, 157159.Google Scholar
Tijms, H. C. (1994). Stochastic Models. An Algebraic Approach. John Wiley, Chichester.Google Scholar
White, D. J. (1988). Mean, variance and probability criteria in finite Markov decision processes: A review. J. Optimization Theory Appl. 56, 129.CrossRefGoogle Scholar