On using hellinger distance in checking the validity of dynamic approximations

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    The purpose of this article is to provide validation for the approximate algebraic propagation algorithms to accommodate non-Gaussian dynamic processes. These algorithms have been developed to carry out Bayesian analysis based on conjugate forms and presented with detailed examples of response distributions such as Poisson and Lognormal. The validity of the approximation algorithms can be checked by introducing a metric (Hellinger divergence measure) over the distribution of the states (parameters) and use it to judge the approximation. Theoretical bounds for the efficacy of such procedure are discussed.

    Original languageEnglish
    Pages (from-to)4179-4186
    Number of pages8
    JournalCommunications in Statistics - Theory and Methods
    Issue number19
    Publication statusPublished - Oct 1 2014


    • Bayesian forecasting
    • Dynamic models
    • Hellinger distance

    ASJC Scopus subject areas

    • Statistics and Probability


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