Порівняння методів

	Метод Монте-Карло для даних з пропусками	Послідовний Монте-Карло
Галузь	Баєсові методи	Баєсові методи
Родина	Bayesian methods	Bayesian methods
Рік появи	1987–2002	1993 (particle filter); 2006 (SMC samplers)
Автор методу	Rubin, D. B. / Little, R. J. A.	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Тип	Simulation-based estimation	Sequential Bayesian computation
Основоположне джерело	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860	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.
Інші назви	MC simulation missing data, Monte Carlo imputation, simulation-based missing data analysis, stochastic simulation with incomplete data	SMC, particle filter, sequential importance resampling, SMC sampler
Пов'язані	6	6
Підсумок	Monte Carlo simulation with missing data combines stochastic simulation — drawing random values from probability distributions — with principled missing-data strategies such as multiple imputation. Instead of discarding incomplete records or substituting a single fill-in value, the method generates many simulated complete datasets, runs the target analysis on each, and pools the results to yield estimates that honestly reflect both sampling uncertainty and uncertainty due to missingness.	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.