Transdimensional sequential Monte Carlo using variational Bayes — SMCVB

McGrory, C.A., Pettitt, A.N., Titterington, D. M., Alston, C.L., & Kelly, M (2016) Transdimensional sequential Monte Carlo using variational Bayes — SMCVB. Computational Statistics and Data Analysis, 93, pp. 246-254.

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Abstract

A new transdimensional Sequential Monte Carlo (SMC) algorithm called SM- CVB is proposed. In an SMC approach, a weighted sample of particles is generated from a sequence of probability distributions which ‘converge’ to the target distribution of interest, in this case a Bayesian posterior distri- bution. The approach is based on the use of variational Bayes to propose new particles at each iteration of the SMCVB algorithm in order to target the posterior more efficiently. The variational-Bayes-generated proposals are not limited to a fixed dimension. This means that the weighted particle sets that arise can have varying dimensions thereby allowing us the option to also estimate an appropriate dimension for the model. This novel algorithm is outlined within the context of finite mixture model estimation. This pro- vides a less computationally demanding alternative to using reversible jump Markov chain Monte Carlo kernels within an SMC approach. We illustrate these ideas in a simulated data analysis and in applications.

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ID Code: 84807
Item Type: Journal Article
Refereed: Yes
Keywords: Transdimensional sequential Monte Carlo, Variational Bayes, Bayesian analysis, Mixture models
DOI: 10.1016/j.csda.2015.03.006
ISSN: 0167-9473
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Institutes > Institute for Future Environments
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 16 Jun 2015 05:20
Last Modified: 09 Mar 2016 04:12

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