Comparar métodos

	Inferência Bayesiana com Dados Ausentes	Computação Bayesiana Aproximada com Dados Ausentes
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	1976–1987	2002 (ABC); 1987 (missing data theory)
Autor original	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)	Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)
Tipo	Bayesian probabilistic model	likelihood-free Bayesian inference
Fonte seminal	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860	Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.
Outros nomes	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model	ABC with missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD
Relacionados	6	6
Resumo	Bayesian 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.