The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Obstacles to High-Dimensional Particle Filtering
Open accessAbstract
Particle filters are ensemble-based assimilation schemes that, unlike the ensemble Kalman filter, employ a fully nonlinear and non-Gaussian analysis step to compute the probability distribution function (pdf) of a system’s state conditioned on a set of observations. Evidence is provided that the ensemble size required for a successful particle filter scales exponentially with the problem size. For the simple example in which each component of the state vector is independent, Gaussian, and of unit variance and the observations are of each state component separately with independent, Gaussian errors, simulations indicate that the required ensemble size scales exponentially with the state dimension. In this example, the particle filter requires at least 1011 members when applied to a 200-dimensional state. Asymptotic results, following the work of Bengtsson, Bickel, and collaborators, are provided for two cases: one in which each prior state component is independent and identically distributed, and one in which both the prior pdf and the observation errors are Gaussian. The asymptotic theory reveals that, in both cases, the required ensemble size scales exponentially with the variance of the observation log likelihood rather than with the state dimension per se.
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Cited by
Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020) · Science · doi:10.1126/science.abb3221 · Cited by 3,633
Particle Filtering in Geophysical Systems
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Forecasting seasonal outbreaks of influenza
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Nonlinear data assimilation in geosciences: an extremely efficient particle filter
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Metadata
- Authors
- Snyder, C., Bengtsson, T., Bickel, P., & Anderson, J.
- Venue
- Monthly Weather Review
- Publisher
- American Meteorological Society
- Published
- 2008-12-01
- Volume
- 136
- Issue
- 12
- Pages
- 4629-4640
- ISSN
- 0027-0644 (ISSN-L)
- 1520-0493
- OpenAlex
- W2150951085
- Cited by
- 696
- References
- 35
- FWCI
- 13.10
- Type
- journal article
- Language
- en
- Open access
- bronze
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