Porovnat metody

	Robustní Markovovy řetězce Monte Carlo	Sekvenční Monte Carlo
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	2000s–2010s	1993 (particle filter); 2006 (SMC samplers)
Tvůrce	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Typ	Bayesian computational sampling	Sequential Bayesian computation
Původní zdroj	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.
Další názvy	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC	SMC, particle filter, sequential importance resampling, SMC sampler
Příbuzné	5	6
Shrnutí	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.