The predictive approach to Bayesian inference is enriched with invariance considerations which naturally occur in experimental designs. Invariance is used extensively both to compute predictive distributions and to derive predictive models together with the posterior parameter distribution. Under the usual assumption of normality, a simple and direct relationship between predictive and posterior distribution is shown to exist. General results are illustrated in detail with reference to the diallel cross experimental design.
Invariance and Bayesian predictive analysis with an application to the diallel cross design
VERONESE, PIERO
1988
Abstract
The predictive approach to Bayesian inference is enriched with invariance considerations which naturally occur in experimental designs. Invariance is used extensively both to compute predictive distributions and to derive predictive models together with the posterior parameter distribution. Under the usual assumption of normality, a simple and direct relationship between predictive and posterior distribution is shown to exist. General results are illustrated in detail with reference to the diallel cross experimental design.File in questo prodotto:
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