Methoden vergleichen

	Robuste Markov-Chain-Monte-Carlo-Verfahren	Sequentielle Monte-Carlo-Methoden
Fachgebiet	Bayes-Statistik	Bayes-Statistik
Familie	Bayesian methods	Bayesian methods
Entstehungsjahr	2000s–2010s	1993 (particle filter); 2006 (SMC samplers)
Urheber	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
Wegweisende Quelle	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.
Aliasnamen	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC	SMC, particle filter, sequential importance resampling, SMC sampler
Verwandt	5	6
Zusammenfassung	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.