방법 비교

	누락 데이터가 있는 근사 베이즈 계산 (Approximate Bayesian Computation with Missing Data)	Multiple Imputation
분야	베이지안	통계학
계열	Bayesian methods	Process / pipeline
기원 연도	2002 (ABC); 1987 (missing data theory)	1987
창시자	Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)	Donald B. Rubin
유형	likelihood-free Bayesian inference	Missing-data handling procedure
원전	Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.	Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
별칭	ABC with missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD	MICE, Multivariate Imputation by Chained Equations, Çoklu Atama (Multiple Imputation — MICE)
관련	6	1
요약	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.	Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.