Comparer des méthodes

	Monte Carlo Séquentiel Robuste	Monte Carlo Hamiltonien
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2000s	1987
Auteur d'origine	Ristic, Arulampalam, Gordon and others (2000s, with ongoing development)	—
Type	Sequential Bayesian sampling algorithm	Gradient-based Markov chain Monte Carlo sampler
Source fondatrice	Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318	Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.
Alias	robust particle filter, robust SMC, outlier-robust particle filtering, heavy-tailed SMC	HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler
Apparentées	6	3
Résumé	Robust Sequential Monte Carlo (Robust SMC) extends standard particle filtering to handle outliers, heavy-tailed noise, and model misspecification in sequential data. By replacing Gaussian likelihood assumptions with heavier-tailed distributions or employing outlier-detection strategies during particle weighting, it maintains accurate state-tracking and parameter estimation even when observations deviate from the assumed model.	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.