Vertaile menetelmiä

	Robust Markov Chain Monte Carlo	Hamiltonin Monte Carlo
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	2000s–2010s	1987
Kehittäjä	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others	—
Tyyppi	Bayesian computational sampling	Gradient-based Markov chain Monte Carlo sampler
Alkuperäislähde	Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.	Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.
Rinnakkaisnimet	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC	HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler
Liittyvät	5	3
Tiivistelmä	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.	Hamiltonian Monte Carlo (HMC) is a gradient-based Markov chain Monte Carlo algorithm that uses the geometry of the log-posterior surface to make large, informed jumps through parameter space instead of the small random steps of classical MCMC. Originally introduced for lattice field theory by Duane, Kennedy, Pendleton, and Roweth (1987) under the name Hybrid Monte Carlo, and brought into mainstream statistics by Radford Neal's authoritative 2011 chapter, HMC is today the default sampler in Stan and PyMC and is widely regarded as the state-of-the-art engine for Bayesian posterior inference in high-dimensional models.