Posterior Predictive Assessment of Model Fitness via Realized Discrepancies¶
Gelman, A., Meng, X.-L, & Stern, H. (1996). Posterior Predictive Assessment of Model Fitness via Realized Discrepancies. Statistica Sinica, 6(4), 733-807.
Retired. This entry was not a citation. Our extractor stored an inline prose definition from the article as though it were a bibliographic record: the title names a concept rather than a published work, and the citation text names no work at all. The page is kept because links to it still resolve, but it is withdrawn from the reference list. This entry is kept so the citations that pointed at it still resolve, and so the correction is visible rather than silent.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Posterior-Predictive Residual Check
- The observed data were used both to fit the model and to check it, so posterior-predictive p-values are conservative — a "pass" is weak evidence, because the model has already been bent toward the very data it is being tested against.
This sourceExplains that posterior-predictive checks reuse the observed data in fitting and checking, yielding typically conservative p-values whose non-rejection does not establish model correctness.
- The observed data were used both to fit the model and to check it, so posterior-predictive p-values are conservative — a "pass" is weak evidence, because the model has already been bent toward the very data it is being tested against.
- Predictive Replication Check
- The idea is the posterior predictive check — simulate data under the fitted model and see whether the real data look like a plausible draw — and its power is precisely that it tests the whole generative story, not one moment of it.
This sourceGelman, Meng, and Stern assess a fitted model by comparing observed discrepancies with posterior predictive reference distributions generated through simulation.
- The idea is the posterior predictive check — simulate data under the fitted model and see whether the real data look like a plausible draw — and its power is precisely that it tests the whole generative story, not one moment of it.
Verification¶
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Links previously used in the corpus¶
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