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المجالبايزيبايزي
العائلةBayesian methodsBayesian methods
سنة النشأة1976–19872002 (ABC); 1987 (missing data theory)
صاحب الطريقةRubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)
النوعBayesian probabilistic modellikelihood-free Bayesian inference
المصدر التأسيسيLittle, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035. link ↗
الأسماء البديلةBayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian modelABC with missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD
ذات صلة66
الملخصBayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us.Approximate Bayesian Computation with missing data extends the likelihood-free ABC framework to settings where observations are incomplete or partially recorded. By simulating data under a posited model and accepting parameter draws whose simulated summary statistics are close to the observed ones, it bypasses the need to evaluate an intractable likelihood — even when some data values are absent.
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ScholarGateقارن الطرق: Bayesian Inference with Missing Data · Approximate Bayesian Computation with Missing Data. استُرجع بتاريخ 2026-06-15 من https://scholargate.app/ar/compare