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    Bayesian Inference for Latent Chain Graphs

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    Type
    Preprint
    Authors
    Lu, Deng
    Iorio, Maria De
    Jasra, Ajay cc
    Rosner, Gary L.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-08-12
    Permanent link to this record
    http://hdl.handle.net/10754/660810
    
    Metadata
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    Abstract
    In this article we consider Bayesian inference for partially observed Andersson-Madigan-Perlman (AMP) Gaussian chain graph (CG) models. Such models are of particular interest in applications such as biological networks and financial time series. The model itself features a variety of constraints which make both prior modeling and computational inference challenging. We develop a framework for the aforementioned challenges, using a sequential Monte Carlo (SMC) method for statistical inference. Our approach is illustrated on both simulated data as well as real case studies from university graduation rates and a pharmacokinetics study.
    Publisher
    Submitted to AIMS
    Journal
    Submitted to Foundations of Data Science
    arXiv
    arXiv:1908.04002
    1908.04002
    Additional Links
    https://arxiv.org/pdf/1908.04002
    Collections
    Preprints; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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