On Counts of Organisms Able to Signal the Presence of an Observer

Kemp, Adrienne W.

doi:10.1002/bimj.4710340508

Cited by 6 publications

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References 5 publications

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“…cf. Kemp (1979Kemp ( ), (1992b, giving rise to the name pseudo-Euler distribution. It does not arise from stochastic processes similar to those for the Heine and Euler distributions, and so is not dealt with in this paper.…”

Section: Introductionmentioning

confidence: 99%

Steady-state Markov chain models for the Heine and Euler distributions

Kemp¹

1992

Journal of Applied Probability

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Applied Probability Trust is collaborating with JSTOR to digitize, preserve and extend access to Journal of Applied Probability. AbstractThe paper puts forward steady-state Markov chain models for the Heine and Euler distributions. The models for oil exploration strategies that were discussed by Benkherouf and Bather (1988) are reinterpreted as current-age models for discrete renewal processes. Steady-state success-runs processes with non-zero probabilities that a trial is abandoned, Foster processes, and equilibrium random walks corresponding to elective M/M/1 queues are also examined. q-ANALOGUES; SUCCESS-RUNS; FOSTER PROCESS; ELECTIVE M/M/1 QUEUE

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Section: Introductionmentioning

confidence: 99%

Steady-state Markov chain models for the Heine and Euler distributions

Kemp¹

1992

Journal of Applied Probability

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Contagious Distributions

Kemp¹,

Kemp²

2005

Encyclopedia of Biostatistics

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Polya proposed a model for the spread of an infection, whereby the probability of catching a disease depended on the existing number of cases. This leads to a distribution that has a limiting negative binomial form. Neyman proposed an alternative approach that arises from a Poisson distribution of clumps, with the number of cells for each clump being Poisson. This article surveys probability distributions and corresponding generating functions that result from such constructions.

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