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Bayesian mixture model estimation of aerosol particle size distributions
Author(s) -
Wraith D.,
Alston C.,
Mengersen K.,
Hussein T.
Publication year - 2011
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.1020
Subject(s) - reversible jump markov chain monte carlo , mixture model , markov chain monte carlo , bayesian probability , aerosol , bayesian inference , inference , point estimation , computer science , statistics , mathematics , statistical physics , artificial intelligence , physics , meteorology
Abstract In this paper, we examine approaches to estimate a Bayesian mixture model at both single and multiple time points for a sample of actual and simulated aerosol particle size distribution (PSD) data. For estimation of a mixture model at a single time point, we use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate mixture model parameters including the number of components which is assumed to be unknown. We compare the results of this approach to a commonly used estimation method in the aerosol physics literature. As PSD data is often measured over time, often at small time intervals, we also examine the use of an informative prior for estimation of the mixture parameters which takes into account the correlated nature of the parameters. The Bayesian mixture model offers a promising approach, providing advantages both in estimation and inference. Copyright © 2009 John Wiley & Sons, Ltd.