Computational aspects of sequential Monte Carlo filter and smoother
Computational aspects of sequential Monte Carlo filter and smoother
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DOI：
10.1007/s10463-014-0446-0
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发表时间：
2014-03
影响因子：
1
通讯作者：
G. Kitagawa
中科院分区：
文献类型：
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作者：
G. Kitagawa
信息技术的进步使得能够将计算机密集型方法应用于统计分析。在时间序列建模中，序贯蒙特卡罗方法是针对一般的非线性非高斯状态空间模型而发展起来的，它使得能够考虑非常复杂的非线性非高斯模型来解决实际问题。在本文中，我们考虑了几个计算问题与顺序蒙特卡罗滤波器和平滑，如使用大量的粒子，两个过滤器公式平滑，并行计算。还考虑了后验均值平滑器和高斯和平滑器。
Progress in information technologies has enabled to apply computer-intensive methods to statistical analysis. In time series modeling, sequential Monte Carlo method was developed for general nonlinear non-Gaussian state-space models and it enables to consider very complex nonlinear non-Gaussian models for real-world problems. In this paper, we consider several computational problems associated with sequential Monte Carlo filter and smoother, such as the use of a huge number of particles, two-filter formula for smoothing, and parallel computation. The posterior mean smoother and the Gaussian-sum smoother are also considered.