Applications of the EM Algorithm to the Estimation of Bayesian Hyperparameters.
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WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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Applications of the EM algorithm to the estimation of Bayesian hyperparameters are discussed and reviewed in the context of the authors philosophy involving the inductive and pragmatic modelling of sampling distributions and prior structures. Frequently the hyperparameters may be estimated from the data, thus avoiding the subjective assessment of these values. The ideas are applied to multiple regression models, histograms and multinomial distributions. A numerical example is described in the context of smoothing the cell probabilities of several multinomial distributions. Author
- Statistics and Probability