方法对比

	稳健马尔可夫链蒙特卡洛 (Robust Markov Chain Monte Carlo)	顺序蒙特卡洛
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2000s–2010s	1993 (particle filter); 2006 (SMC samplers)
提出者	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
类型	Bayesian computational sampling	Sequential Bayesian computation
开创性文献	Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.	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.
别名	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC	SMC, particle filter, sequential importance resampling, SMC sampler
相关	5	6
摘要	Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates.	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.