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On Binomial Observations of Continuous-Time Markovian Population Models

  • N. G. Bean (a1), R. Elliott (a1), A. Eshragh (a2) and J. V. Ross (a1)
Abstract

In this paper we consider a class of stochastic processes based on binomial observations of continuous-time, Markovian population models. We derive the conditional probability mass function of the next binomial observation given a set of binomial observations. For this purpose, we first find the conditional probability mass function of the underlying continuous-time Markovian population model, given a set of binomial observations, by exploiting a conditional Bayes' theorem from filtering, and then use the law of total probability to find the former. This result paves the way for further study of the stochastic process introduced by the binomial observations. We utilize our results to show that binomial observations of the simple birth process are non-Markovian.

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Corresponding author
Postal address: School of Mathematical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia.
∗∗ Postal address: School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia Email address: ali.eshragh@newcastle.edu.au
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Journal of Applied Probability
  • ISSN: 0021-9002
  • EISSN: 1475-6072
  • URL: /core/journals/journal-of-applied-probability
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