Transdimensional sequential Monte Carlo using variational Bayes - SMCVB

McGrory, C. A., Pettitt, A. N., Titterington, D. M., Alston, C. L. and Kelly, M. (2016) Transdimensional sequential Monte Carlo using variational Bayes - SMCVB. Computational Statistics and Data Analysis, 93 246-254. doi:10.1016/j.csda.2015.03.006

Author McGrory, C. A.
Pettitt, A. N.
Titterington, D. M.
Alston, C. L.
Kelly, M.
Title Transdimensional sequential Monte Carlo using variational Bayes - SMCVB
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2016-01-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.csda.2015.03.006
Open Access Status Not Open Access
Volume 93
Start page 246
End page 254
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2016
Language eng
Abstract A new transdimensional Sequential Monte Carlo (SMC) algorithm called SMCVB 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 distribution. 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 provides 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.
Keyword Transdimensional sequential Monte Carlo
Variational Bayes
Bayesian analysis
Mixture models
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Queensland Alliance for Agriculture and Food Innovation
Official 2016 Collection
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