(Enter summary)
Abstract: Data assimilation is formulated in a Bayesian context. This leads to
a sampling problem in the space of continuous time paths. By writing
down a density in path space, and conditioning on observations, it is possible
to define a range of Markov Chain Monte Carlo (MCMC) methods
which sample from the desired distribution in path space, and thereby
solve the data assimilation problem. The basic building-blocks for the
MCMC methods that we concentrate on in this paper are stochastic partial... (Update)
Cited by: More
Analysis of SPDEs Arising in Path Sampling, Part II: The.. - Hairer, Stuart, Voss
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0.8: Analysis of SPDEs Arising in Path Sampling, Part I: The .. - Hairer, Stuart, Voss.. (2005)
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0.5: Conditional Path Sampling of SDEs and the Langevin MCMC Method - Stuart, Voss, Wiberg (2004)
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BibTeX entry: (Update)
M. Hairer, A. M. Stuart, and J. Voss. A Bayesian approach to data assimilation. submitted, 2005. http://citeseer.ist.psu.edu/hairer05bayesian.html More
@misc{ hairer05bayesian,
author = "M. Hairer and A. Stuart and J. Voss",
title = "A Bayesian approach to data assimilation",
text = "M. Hairer, A. M. Stuart, and J. Voss. A Bayesian approach to data assimilation.
submitted, 2005.",
year = "2005",
url = "citeseer.ist.psu.edu/hairer05bayesian.html" }
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Documents on the same site (http://seehuhn.de/mathe/): More
Conditional Path Sampling of SDEs and the Langevin MCMC Method - Stuart, Voss, Wiberg (2004)
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Some Large Deviation Results for Diffusion Processes - Voß (2004)
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Interpreting the parameters of the diffusion model: An.. - Voss, Rothermund, Voss (2004)
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