方法对比
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| 缺失数据的序贯蒙特卡洛方法× | 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 (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 类型≠ | Sequential Bayesian filtering / smoothing | 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: 978-0387951461 | 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 missing data, particle filter with missing observations, SMC missing observations, particle smoothing with incomplete data | SMC, particle filter, sequential importance resampling, SMC sampler |
| 相关 | 6 | 6 |
| 摘要≠ | Sequential Monte Carlo (SMC) with missing data extends the standard particle filter to state-space models in which some observations are absent. When an observation is missing at a given time step the update step is simply skipped: particles are propagated forward through the transition model without reweighting, preserving exact Bayesian inference under any missing-data pattern as long as missingness is ignorable (missing at random or missing completely at random). | 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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