Salīdzināt metodes

	Robusta Markova ķēžu Montekarlo metode	Mārkova ķēžu Montekarlo (MCMC)
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2000s–2010s	—
Autors	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others	—
Tips	Bayesian computational sampling	Posterior sampling algorithm
Pirmavots	Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Citi nosaukumi	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Saistītās	5	3
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.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.