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    Adaptive Sequential MCMC for Combined State and Parameter Estimation

    Access Status
    Fulltext not available
    Authors
    Cao, Zhanglong
    Bryant, David
    Parry, Matthew
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Cao, Z. and Bryant, D. and Parry, M. 2018. Adaptive Sequential MCMC for Combined State and Parameter Estimation. arXiv.org. 1803.07734: pp. 1-44.
    Source Title
    arXiv.org
    Faculty
    Faculty of Science and Engineering
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/78086
    Collection
    • Curtin Research Publications
    Abstract

    In the case of a linear state space model, we implement an MCMC sampler with two phases. In the learning phase, a self-tuning sampler is used to learn the parameter mean and covariance structure. In the estimation phase, the parameter mean and covariance structure informs the proposed mechanism and is also used in a delayed-acceptance algorithm. Information on the resulting state of the system is given by a Gaussian mixture. In on-line mode, the algorithm is adaptive and uses a sliding window approach to accelerate sampling speed and to maintain appropriate acceptance rates. We apply the algorithm to joined state and parameter estimation in the case of irregularly sampled GPS time series data.

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