Journal of Applied Probability

On binomial observations of continuous-time Markovian population models

N. G. Bean, R. Elliott, A. Eshragh, and J. V. Ross

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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.

Article information

J. Appl. Probab. Volume 52, Number 2 (2015), 457-472.

First available in Project Euclid: 23 July 2015

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 60J27: Continuous-time Markov processes on discrete state spaces
Secondary: 60J80: Branching processes (Galton-Watson, birth-and-death, etc.) 62M09: Non-Markovian processes: estimation 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]

Continuous-time Markovian population model binomial observation simple birth process filtering


Bean, N. G.; Elliott, R.; Eshragh, A.; Ross, J. V. On binomial observations of continuous-time Markovian population models. J. Appl. Probab. 52 (2015), no. 2, 457--472. doi:10.1239/jap/1437658609.

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