Bayesian mixture model estimation of aerosol particle size distributions


Autoria(s): Wraith, D.; Alston, C.; Mengersen, K.; Hussein, T.
Data(s)

2011

Resumo

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.

Identificador

http://eprints.qut.edu.au/92581/

Publicador

John Wiley & Sons, Ltd

Relação

DOI:10.1002/env.1020

Wraith, D., Alston, C., Mengersen, K., & Hussein, T. (2011) Bayesian mixture model estimation of aerosol particle size distributions. Environmetrics, 22(1), pp. 23-34.

Direitos

Copyright 2009 John Wiley & Sons, Ltd.

Fonte

Faculty of Health; Institute of Health and Biomedical Innovation; School of Mathematical Sciences; Science & Engineering Faculty; School of Public Health & Social Work

Palavras-Chave #Bayesian #mixture model #particle size distributions
Tipo

Journal Article