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| 로버스트 순차 몬테카를로× | 해밀토니안 몬테카를로× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 2000s | 1987 |
| 창시자≠ | Ristic, Arulampalam, Gordon and others (2000s, with ongoing development) | — |
| 유형≠ | Sequential Bayesian sampling algorithm | Gradient-based Markov chain Monte Carlo sampler |
| 원전≠ | 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. DOI ↗ |
| 별칭≠ | robust particle filter, robust SMC, outlier-robust particle filtering, heavy-tailed SMC | HMC, Hybrid Monte Carlo, NUTS, No-U-Turn Sampler |
| 관련≠ | 6 | 3 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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