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Bayesian inference for a partially observed birth-death process using data on proportions

Lookup NU author(s): Professor Richard Boys, Dr Colin GillespieORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Wiley-Blackwell, 2018.

For re-use rights please refer to the publisher's terms and conditions.


© 2018 John Wiley & Sons Australia, Ltd. Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be estimated from observed data. Ideally we would have measurements on all interacting chemical species in the process, observed continuously in time. However, in practice, measurements are taken only at a relatively few time-points. In some situations, only very limited observation of the process is available, for example settings in which experimenters can only observe noisy observations on the proportion of cells that are alive. This makes the inference task even more problematic. We consider a range of data-poor scenarios and investigate the performance of various computationally intensive Bayesian algorithms in determining the posterior distribution using data on proportions from a simple birth-death process.

Publication metadata

Author(s): Boys RJ, Ainsworth HF, Gillespie CS

Publication type: Article

Publication status: Published

Journal: Australian and New Zealand Journal of Statistics

Year: 2018

Volume: 60

Issue: 2

Pages: 157-173

Print publication date: 01/06/2018

Online publication date: 30/05/2018

Acceptance date: 02/04/2018

Date deposited: 13/06/2018

ISSN (print): 1369-1473

ISSN (electronic): 1467-842X

Publisher: Wiley-Blackwell


DOI: 10.1111/anzs.12230


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