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A comparison of approximate Bayesian forecasting methods for non‐Gaussian time series
Authors:Raffaella Settimi  Jim Q Smith
Abstract:We present the results on the comparison of efficiency of approximate Bayesian methods for the analysis and forecasting of non‐Gaussian dynamic processes. A numerical algorithm based on MCMC methods has been developed to carry out the Bayesian analysis of non‐linear time series. Although the MCMC‐based approach is not fast, it allows us to study the efficiency, in predicting future observations, of approximate propagation procedures that, being algebraic, have the practical advantage of being very quick. Copyright © 2000 John Wiley & Sons, Ltd.
Keywords:Dynamic generalized linear models  sequential approximation  Poisson time series  Gibbs sampling
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