Computational aspects of sequential Monte Carlo filter and smoother

Genshiro Kitagawa ()

Annals of the Institute of Statistical Mathematics, 2014, vol. 66, issue 3, 443-471

Abstract: 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. Copyright The Institute of Statistical Mathematics, Tokyo 2014

Keywords: Nonlinear non-Gaussian state-space model; Particle filter; Gaussian-sum filter; Two-filter formula; Parallel computation; Posterior mean smoother (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10463-014-0446-0

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