方法对比
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| 缺失数据的序贯蒙特卡洛方法× | 动态序贯蒙特卡洛法× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1993–2001 | 2006 |
| 提出者≠ | Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s) | Del Moral, Doucet, Jasra |
| 类型≠ | Sequential Bayesian filtering / smoothing | Sequential Monte Carlo sampler for dynamic settings |
| 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 | Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436. DOI ↗ |
| 别名 | SMC with missing data, particle filter with missing observations, SMC missing observations, particle smoothing with incomplete data | Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle 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). | Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models. |
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