方法对比
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| 带测量误差的序贯蒙特卡洛× | 顺序蒙特卡洛× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1993–2001 | 1993 (particle filter); 2006 (SMC samplers) |
| 提出者≠ | Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 类型≠ | Sequential Bayesian filtering | Sequential Bayesian computation |
| 开创性文献≠ | Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer New York. ISBN: 978-0-387-95146-1 | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113. DOI ↗ |
| 别名 | SMC with measurement error, particle filter with noisy observations, SMC state-space measurement error, sequential particle filtering with observation noise | SMC, particle filter, sequential importance resampling, SMC sampler |
| 相关 | 6 | 6 |
| 摘要≠ | Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains the noisy measurement, and produces a full posterior distribution over the latent state at every time point. | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. |
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