Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions

DOI10.1214/13-AOAS678zbMath1454.62102arXiv1404.4244WikidataQ56994182 ScholiaQ56994182MaRDI QIDQ2453667

Tareq Hussein, Darren Wraith, Judith Rousseau, C. L. Alston, Kerrie L. Mengersen

Publication date: 10 June 2014

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.4244

zbMATH Keywords

time seriesBayesian statisticsmixture modelsaerosol particle size distribution

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (1)

Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions

Cites Work

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• Asymptotic Behaviour of the Posterior Distribution in Overfitted Mixture Models
• Bayesian forecasting and dynamic models.
• Model based labeling for mixture models
• Allowing for the effect of data binning in a Bayesian normal mixture model
• Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images
• Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
• Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Finite mixture and Markov switching models.
• Bayesian dynamic modeling of latent trait distributions
• An extension of the Dirichlet prior for the analysis of longitudinal multinomial data
• Hidden Markov Models and Disease Mapping
• On Optimality Properties of the Power Prior
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
• Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
• Maximum a posteriori sequence estimation using Monte Carlo particle filters
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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