Bayesian methodsBayesian / computational
动态序贯蒙特卡洛法
动态序贯蒙特卡洛法（Dynamic Sequential Monte Carlo, Dynamic SMC）是一种贝叶斯计算方法，它在接收新观测数据时，维护并更新一组带权重的样本——即粒子。该方法通过动态系统模型传播粒子，根据粒子与观测数据的匹配程度对其进行重加权，并周期性地进行重采样以将计算资源集中在高概率区域，从而为状态空间和时变模型提供在线后验推断。
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来源
- 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: 10.1111/j.1467-9868.2006.00553.x ↗
- Doucet, A., de Freitas, N. & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461
如何引用本页
ScholarGate. (2026, June 3). Dynamic Sequential Monte Carlo Sampler. ScholarGate. https://scholargate.app/zh/bayesian/dynamic-sequential-monte-carlo
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 动态贝叶斯推断贝叶斯↔ compare
- Gibbs Sampling贝叶斯↔ compare
- Hamiltonian Monte Carlo贝叶斯↔ compare
- 卡尔曼滤波器贝叶斯↔ compare
- 粒子滤波器（序贯蒙特卡洛）贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare