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  • Transdimensional sequential Monte Carlo using variational Bayes - SMCVB

    Author(s)
    McGrory, CA
    Pettitt, AN
    Titterington, DM
    Alston, CL
    Kelly, M
    Griffith University Author(s)
    Alston-Knox, Clair L.
    Year published
    2016
    Metadata
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    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 ...
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    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.
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    Journal Title
    Computational Statistics and Data Analysis
    Volume
    93
    DOI
    https://doi.org/10.1016/j.csda.2015.03.006
    Subject
    Statistics
    Statistics not elsewhere classified
    Econometrics
    Publication URI
    http://hdl.handle.net/10072/172832
    Collection
    • Journal articles

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