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