Bayesian prediction of rainfall records using the generalized exponential distribution

SUMMARY 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. Copyright © 2007 John Wiley & Sons, Ltd.

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