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A Bayesian Approach to Data Assimilation (2005)  (Make Corrections)  (1 citation)
M. Hairer, A. M. Stuart, and J. Voss August 30, 2005



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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   (Correct)

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0.5:   Conditional Path Sampling of SDEs and the Langevin MCMC Method - Stuart, Voss, Wiberg (2004)   (Correct)
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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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Conditional Path Sampling of SDEs and the Langevin MCMC Method - Stuart, Voss, Wiberg (2004)   (Correct)
Some Large Deviation Results for Diffusion Processes - Voß (2004)   (Correct)
Interpreting the parameters of the diffusion model: An.. - Voss, Rothermund, Voss (2004)   (Correct)

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