Módszerek összehasonlítása

	Robuszt Hamiltonian Monte Carlo	Hamiltonian Monte Carlo
Tudományterület	Bayes-statisztika	Bayes-statisztika
Módszercsalád	Bayesian methods	Bayesian methods
Keletkezés éve	2010s–2020s	1987
Megalkotó	Livingstone, Zanella and related researchers building on Duane et al. (1987)	—
Típus	Robust MCMC sampler	Gradient-based Markov chain Monte Carlo sampler
Alapmű	Livingstone, S. & Zanella, G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B, 84(2), 496–523.	Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.
Alternatív nevek	Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC	HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler
Kapcsolódó	4	3
Összefoglaló	Robust Hamiltonian Monte Carlo (Robust HMC) is a family of extensions to standard HMC designed to maintain geometric ergodicity and sampling efficiency when the posterior has heavy tails, strong curvature variation, or near-degenerate geometry. By modifying the kinetic energy, mass matrix, or proposal mechanism, these methods ensure reliable exploration of difficult posteriors that defeat the standard NUTS/HMC sampler.	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.