论文标题
顺序蒙特卡洛算法的方差估计：向后抽样方法
Variance estimation for Sequential Monte Carlo Algorithms: a backward sampling approach
论文作者
论文摘要
在本文中，我们考虑了粒子过滤和平滑的在线渐近方差估计的问题。粒子过滤器的当前溶液依赖于粒子家谱，并且在实践中不稳定或难以调节。我们建议通过基于所谓的向后权重引入新的渐近方差估计值来减轻这些局限性。所得估计器的一致性较弱，并且可以进行计算成本以获得更大的稳定性和差异。我们还提出了一个受Olsson＆Westerborn的巴黎算法启发的更高效的估计器。作为一种应用，考虑了粒子平滑，并提供了向后滤波向后平滑估计器的渐近方差的估计器，并提供了应用于加性功能的估计值。
In this paper, we consider the problem of online asymptotic variance estimation for particle filtering and smoothing. Current solutions for the particle filter rely on the particle genealogy and are either unstable or hard to tune in practice. We propose to mitigate these limitations by introducing a new estimator of the asymptotic variance based on the so called backward weights. The resulting estimator is weakly consistent and trades computational cost for more stability and reduced variance. We also propose a more computationally efficient estimator inspired by the PaRIS algorithm of Olsson & Westerborn. As an application, particle smoothing is considered and an estimator of the asymptotic variance of the Forward Filtering Backward Smoothing estimator applied to additive functionals is provided.
