Bayesian mixture model estimation of aerosol 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.
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|Item Type:||Journal Article|
|Keywords:||Bayesian, mixture model, particle size distributions|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2009 John Wiley & Sons, Ltd.|
|Deposited On:||05 Feb 2016 01:32|
|Last Modified:||09 Feb 2016 05:25|
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