Bayesian mixture model estimation of aerosol particle size distributions
Article first published online: 27 AUG 2009
Copyright © 2009 John Wiley & Sons, Ltd.
Volume 22, Issue 1, pages 23–34, February 2011
How to Cite
Wraith, D., Alston, C., Mengersen, K. and Hussein, T. (2011), Bayesian mixture model estimation of aerosol particle size distributions. Environmetrics, 22: 23–34. doi: 10.1002/env.1020
- Issue published online: 27 AUG 2009
- Article first published online: 27 AUG 2009
- Manuscript Accepted: 26 JUN 2009
- Manuscript Received: 22 MAY 2008
- mixture model;
- particle size distributions
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.