Bayesian prediction of rainfall records using the generalized exponential distribution

Mohamed T. Madi, Mohammad Z. Raqab

    Research output: Contribution to journalArticlepeer-review

    29 Citations (Scopus)


    The Los Angeles rainfall data are found to fit well to the two-parameter generalized exponential (GE) distribution. A Bayesian parametric approach is described and used to predict the behavior of further rainfall records. Importance sampling is used to estimate the model parameters, and the Gibbs and Metropolis samplers are used to implement the prediction procedure.

    Original languageEnglish
    Pages (from-to)541-549
    Number of pages9
    Issue number5
    Publication statusPublished - Aug 2007


    • Bayesian estimation
    • Bayesian prediction
    • Generalized exponential distribution
    • Gibbs and Metropolis sampling
    • Importance sampling
    • Record statistics

    ASJC Scopus subject areas

    • Statistics and Probability
    • Ecological Modelling


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