Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-25T06:18:37.492Z Has data issue: false hasContentIssue false

Some general results on random walks, with genetic applications

Published online by Cambridge University Press:  09 April 2009

P. A. P. Moran
Affiliation:
Institute of Advanced Studies, The Australian National University, Canbera
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Consider a random Markovian process in which the state of the system is defined by a random variable which can take the finite set of values i=0, 1, …, N, and which is such that transition can only occur from any state i to the two nearest states i+1. This restriction brings about an essential simplification of the theory for the basic reason that in order for the system to move from i to state j (i < j say) it must first move to i+1, then i+2 and so on until it reaches j. From this it follows that the first passage distribution from i to j is the convolution of the first passage distributions from i to i+l, i+l to i+2,…, j—l to j each of which is comparatively easy to find.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1963

References

[1]Chung, K. L., Markov Chains with stationary transition probabilities. Grundlehren der Mathematischen Wissenschaften. Band 104. Springer (1960).CrossRefGoogle Scholar
[2]Harris, T. E., First passage and recurrence distributions. Trans. Amer. Math. Soc. 73, (1952) 471486.CrossRefGoogle Scholar
[3]Heathcote, C. R. and Moyal, J. E., The random walk (in continuous time) and its application to the theory of queues. Biometrika 46, (1959) 400411.CrossRefGoogle Scholar
[4]Hodges, J. L. and Rosenblatt, M., Recurrence time moments in random walks. Pacific J. Math. 3, (1953) 127136.CrossRefGoogle Scholar
[5]John, P. W. M., The quadratic birth process (Abstract). Ann. Math. Statistics 27, (1956) 865.Google Scholar
[6]Karlin, S. and McGregor, J. L., The differential equations of birth-and-death processes and the Stieltjes moment problem. Trans. Amer. Math. Soc. 85, (1957) 489546.CrossRefGoogle Scholar
[7]Karlin, S. and McGregor, J. L., The classification of birth-and-death processes. Trans. Amer. Math. Soc. 86, (1957) 366400.CrossRefGoogle Scholar
[8]Karlin, S. and McGregor, J. L., On a genetics model of Moran. Proc. Cam. Phil. Soc. 58, (1962).CrossRefGoogle Scholar
[9]Kendall, D. G., Stochastic processes and population growth. Jour. Roy. Statist. Soc. B. 11, (1949) 230264.Google Scholar
[10]Moran, P. A. P., Random processes in genetics. Proc. Cam. Phil. Soc. 54, (1958) 6071.CrossRefGoogle Scholar
[11]Moran, P. A. P., The statistical processes of evolutionary theory. Oxford University Press. 1962.Google Scholar
[12]Muir, T., History of the Theory of Determinants. Vol. 3, (1911), p. 432.Google Scholar
[13]Painvin, L., Sur un certain système d'équations linéaires. Jour. de Math. (de Liouville) (2) iii, (1858), 4146.Google Scholar
[14]Prendiville, B. J., Discussion of a paper by D. G. Kendall. Jour. Roy. Stat. Soc. B. 11, (1949) 273.Google Scholar
[15]Takashima, M., Note on evolutionary processes. Bull. Math. Stat. 7, (1956) 1824.CrossRefGoogle Scholar
[16]Watterson, G. A., Markov chains with absorbing states: a genetic example. Ann. Math. Stat. 32 (1961) 716729.CrossRefGoogle Scholar