Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-10T17:20:10.308Z Has data issue: false hasContentIssue false

First-passage time of Markov processes to moving barriers

Published online by Cambridge University Press:  14 July 2016

Henry C. Tuckwell*
Affiliation:
Monash University
Frederic Y. M. Wan*
Affiliation:
University of British Columbia
*
Postal address: Department of Mathematics, Monash University, Clayton, VIC 3168, Australia.
∗∗Present address: Applied Mathematics Program, FS-20, University of Washington, Seattle, WA 98195, USA.

Abstract

The first-passage time of a Markov process to a moving barrier is considered as a first-exit time for a vector whose components include the process and the barrier. Thus when the barrier is itself a solution of a differential equation, the theory of first-exit times for multidimensional processes may be used to obtain differential equations for the moments and density of the first-passage time of the process to the barrier. The procedure is first illustrated for first-passage-time problems where the solutions are known. The mean first-passage time of an Ornstein–Uhlenbeck process to an exponentially decaying barrier is then found by numerical solution of a partial differential equation. Extensions of the method to problems involving Markov processes with discontinuous sample paths and to cases where the process is confined between two moving barriers are also discussed.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1984 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Research partly supported by NSERC of Canada Operating Grant No. A9259 and by U.S. NSF Grant No. MCS-8306592.

References

Blake, I. F. and Lindsey, W. C. (1973) Level crossing problems for random processes. IEEE Trans. Information Theory 19, 295315.CrossRefGoogle Scholar
Cox, D. R. and Miller, H. D. (1965) The Theory of Stochastic Processes. Wiley, New York.Google Scholar
Crow, J. F. and Kimura, M. (1970) An Introduction to Population Genetics Theory. Harper and Row, New York.Google Scholar
Darling, D. A. and Siegert, A. J. F. (1953) The first passage problem for a continuous Markov process. Ann. Math. Statist. 24, 624639.CrossRefGoogle Scholar
Durbin, J. (1971) Boundary-crossing probabilities for the Brownian motion and Poisson processes and techniques for computing the power of the Kolmogorov-Smirnov test. J. Appl. Prob. 8, 431453.CrossRefGoogle Scholar
Dynkin, E. B. (1965) Markov Processes, I and II. Springer-Verlag, Berlin.Google Scholar
Ewens, W. J. (1979) Mathematical Population Genetics. Springer-Verlag, Berlin.Google Scholar
Ferebee, B. (1982) The tangent approximation to one-sided Brownian exit densities. Z. Wahrscheinlichkeitsth 61, 309326.CrossRefGoogle Scholar
Gihman, I. I. and Skorohod, A. V. (1972) Stochastic Differential Equations. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Gluss, B. (1967) A model for neuron firing with exponential decay of potential resulting in diffusion equations for probability density. Bull. Math. Biophys. 29, 233243.CrossRefGoogle Scholar
Holden, A. V. (1976) Models of the Stochastic Activity of Neurones. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Jaswinski, A. H. (1970) Stochastic Processes and Filtering Theory. Academic Press, New York.Google Scholar
Roy, B. K. and Smith, D. R. (1969) Analysis of exponential decay model of the neuron showing frequency threshold effects. Bull. Math. Biophys. 31, 341357.CrossRefGoogle ScholarPubMed
Stein, R. B. (1965) A theoretical analysis of neuronal variability. Biophys. J. 5, 173194.CrossRefGoogle ScholarPubMed
Tuckwell, H. C. (1974) A study of some diffusion models of population growth. Theoret. Popn Biol. 5, 345357.CrossRefGoogle ScholarPubMed
Tuckwell, H. C. (1976) The effects of random selection on gene frequency. Math. Biosci. 30, 113126.CrossRefGoogle Scholar
Tuckwell, H. C., Wan, F. Y. M. and Wong, Y. S. (1984) The interspike interval of a cable model neuron with white noise input. Biol. Cybernet. 49, 155167.CrossRefGoogle ScholarPubMed
Wan, F. Y. M., Wong, Y. S. and Tuckwell, H. C. (1983) Firing time of spatially distributed neurons and vector valued diffusion processes. I.A.M.S. Technical Report.Google Scholar
Weiss, R. D. (1964) A model for firing patterns of auditory nerve fibers. M.I.T.—Research Lab. of Electronics, Tech. Report #418.Google Scholar
Wilbur, W. J. and Rinzel, J. (1983) A theoretical basis for large coefficient of variation and bimodality in neuronal interspike interval distributions. J. Theoret. Biol. 105, 345368.CrossRefGoogle ScholarPubMed