Comparar métodos

	Cálculo bayesiano aproximado con datos faltantes	Monte Carlo Secuencial
Campo	Bayesiano	Bayesiano
Familia	Bayesian methods	Bayesian methods
Año de origen	2002 (ABC); 1987 (missing data theory)	1993 (particle filter); 2006 (SMC samplers)
Autor original	Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tipo	likelihood-free Bayesian inference	Sequential Bayesian computation
Fuente seminal	Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
Alias	ABC with missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD	SMC, particle filter, sequential importance resampling, SMC sampler
Relacionados	6	6
Resumen	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.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.