A Generalization of the Bayesian Steady Forecasting Model
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1979 · Volume 41 · Issue 3
DOI: 10.1111/j.2517-6161.1979.tb01092.x
Abstract
The Bayesian Steady Forecasting model is generalized to a very wide class of processes other than the normal by defining the time series on the decision space. Examples of such processes are presented including a Beta-Binomial process, a Poisson-Gamma process and a Student-tsample distribution steady model. Simple updating relations are given for most of the processes discussed.
Keywords
bayesian forecasting, multistate processes, time dependent poisson process, time dependent binomial process, time dependent student-tprocess, exponentially weighted moving averages, bounded loss functions
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