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• W2019979720 abstract "Recent approaches mixing frequentist principles with bayesian inference propose internal goodness-of-fit (GOF) p-values that might be valuable for critical analysis of bayesian statistical models. However, GOF p-values developed to date only have known probability distributions under restrictive conditions. As a result, no known GOF p-value has a known probability distribution for any discrepancy function.We show mathematically that a new GOF p-value, called the sampled posterior p-value (SPP), asymptotically has a uniform probability distribution whatever the discrepancy function. In a moderate finite sample context, simulations also showed that the SPP appears stable to relatively uninformative misspecifications of the prior distribution.These reasons, together with its numerical simplicity, make the SPP a better canonical GOF p-value than existing GOF p-values." @default.
• W2019979720 created "2016-06-24" @default.
• W2019979720 creator A5029220878 @default.
• W2019979720 date "2011-03-18" @default.
• W2019979720 modified "2023-09-26" @default.
• W2019979720 title "A New Calibrated Bayesian Internal Goodness-of-Fit Method: Sampled Posterior p-Values as Simple and General p-Values That Allow Double Use of the Data" @default.
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• W2019979720 doi "https://doi.org/10.1371/journal.pone.0014770" @default.
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