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A Potential Implicit Particle Method for High-dimensional Systems : Volume 20, Issue 6 (28/11/2013)

By Weir, B.

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Book Id: WPLBN0003987085
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: A Potential Implicit Particle Method for High-dimensional Systems : Volume 20, Issue 6 (28/11/2013)  
Author: Weir, B.
Volume: Vol. 20, Issue 6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Miller, R. N., Spitz, Y. H., & Weir, B. (2013). A Potential Implicit Particle Method for High-dimensional Systems : Volume 20, Issue 6 (28/11/2013). Retrieved from http://hawaiilibrary.net/


Description
Description: College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA. This paper presents a particle method designed for high-dimensional state estimation. Instead of weighing random forecasts by their distance to given observations, the method samples an ensemble of particles around an optimal solution based on the observations (i.e., it is implicit). It differs from other implicit methods because it includes the state at the previous assimilation time as part of the optimal solution (i.e., it is a lag-1 smoother). This is accomplished through the use of a mixture model for the background distribution of the previous state. In a high-dimensional, linear, Gaussian example, the mixture-based implicit particle smoother does not collapse. Furthermore, using only a small number of particles, the implicit approach is able to detect transitions in two nonlinear, multi-dimensional generalizations of a double-well. Adding a step that trains the sampled distribution to the target distribution prevents collapse during the transitions, which are strongly nonlinear events. To produce similar estimates, other approaches require many more particles.

Summary
A potential implicit particle method for high-dimensional systems

Excerpt
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