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Bayesian inference for a partially observed birth–death process using data on proportions
Author(s) -
Boys Richard J.,
Ainsworth Holly F.,
Gillespie Colin S.
Publication year - 2018
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12230
Subject(s) - inference , bayesian inference , bayesian probability , range (aeronautics) , process (computing) , mathematics , birth–death process , posterior probability , task (project management) , statistics , computer science , machine learning , artificial intelligence , population , materials science , demography , management , sociology , economics , composite material , operating system
Summary 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.