方法对比

	鲁棒序贯蒙特卡洛	Hamiltonian Monte Carlo
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2000s	1987
提出者	Ristic, Arulampalam, Gordon and others (2000s, with ongoing development)	—
类型	Sequential Bayesian sampling algorithm	Gradient-based Markov chain Monte Carlo sampler
开创性文献	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.
别名	robust particle filter, robust SMC, outlier-robust particle filtering, heavy-tailed SMC	HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler
相关	6	3
摘要	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.