Salīdzināt metodes

	Robusta Markova ķēžu Montekarlo metode	Robustā Bēsa secināšana
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2000s–2010s	1984–1990
Autors	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others	James O. Berger
Tips	Bayesian computational sampling	Bayesian sensitivity / robustness framework
Pirmavots	Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.
Citi nosaukumi	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes
Saistītās	5	6
Kopsavilkums	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.	Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.