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• W3106202655 abstract "Models are often defined through conditional rather than joint distributions, but it can be difficult to check whether the conditional distributions are compatible, i.e. whether there exists a joint probability distribution which generates them. When they are compatible, a Gibbs sampler can be used to sample from this joint distribution. When they are not, the Gibbs sampling algorithm may still be applied, resulting in a pseudo-Gibbs sampler. We show its stationary probability distribution to be the optimal compromise between the conditional distributions, in the sense that it minimizes a mean squared misfit between them and its own conditional distributions. This allows us to perform Objective Bayesian analysis of correlation parameters in Kriging models by using univariate conditional Jeffreys-rule posterior distributions instead of the widely used multivariate Jeffreys-rule posterior. This strategy makes the full-Bayesian procedure tractable. Numerical examples show it has near-optimal frequentist performance in terms of prediction interval coverage." @default.
• W3106202655 created "2020-11-23" @default.
• W3106202655 creator A5024069297 @default.
• W3106202655 date "2019-01-01" @default.
• W3106202655 modified "2023-10-01" @default.
• W3106202655 title "Optimal compromise between incompatible conditional probability distributions, with application to Objective Bayesian Kriging" @default.
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• W3106202655 doi "https://doi.org/10.1051/ps/2018023" @default.
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