Analysis of SPDEs arising in Path Sampling, Part I: The Gaussian Case
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| details:
| Martin Hairer, Andrew M. Stuart, Jochen Voss and Petter Wiberg:
Analysis of SPDEs arising in Path Sampling, Part I: The Gaussian Case.
Communications in Mathematical Sciences, vol. 3, no. 4,
pp. 587–603, 2005.
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| online:
| article, journal
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| preprint:
| pdf, ps, arXiv
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| BibTeX, MathSciNet, Google
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| keywords:
| SPDEs, conditioned diffusions, Gaussian Processes, Kalman-Bucy filter, high dimensional sampling
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| MSC2000:
| 60H15, 60G15, 60G35, 60H10
|
Abstract
In many applications it is important to be able to sample paths of SDEs
conditional on observations of various kinds. This paper studies SPDEs
which solve such sampling problems. The SPDE may be viewed as an infinite
dimensional analogue of the Langevin SDE used in finite dimensional
sampling. Here the theory is developed for conditioned Gaussian processes
for which the resulting SPDE is linear. Applications include the
Kalman-Bucy filter/smoother. A companion paper studies the nonlinear case,
building on the linear analysis provided here.
Citations
I believe that this work is cited in the following texts.
If you know of any more citations, please let me know.
- A. Beskos and A.M. Stuart:
Computational complexity of Metropolis-Hastings methods in high dimensions.
Pages 61–72 in Proceedings of MCQMC08,
Pierre L'Ecuyer and Art B. Owen (editors), 2010.
link
- D. White and A.M. Stuart:
Green's Functions by Monte Carlo.
Pages 627–637 in Proceedings of MCQMC08,
Pierre L'Ecuyer and Art B. Owen (editors), 2010.
link
- A. Beskos, G.O. Roberts and A.M. Stuart:
Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions.
Ann. Appl. Probab., vol. 19, no. 3, pp. 863–898, 2009.
online
- M. Hairer, A.M. Stuart and J. Voss:
Signal Processing Problems on Function Space: Bayesian Formulation, Stochastic PDEs and Effective MCMC Methods.
To appear in The Oxford Handbook of Nonlinear Filtering (editors Dan Crisan and Boris Rozovsky),
2009.
preprint, more…
- M. Hairer, A.M. Stuart and J. Voss:
Sampling Conditioned Diffusions.
Pages 159–186 in Trends in Stochastic Analysis,
Cambridge University Press,
vol. 353 of London Mathematical Society Lecture Note Series, 2009.
link, preprint, more…
- H. Weber:
Sharp interface limit for invariant measures of a stochastic Allen-Cahn equation.
Preprint, 2009.
preprint
- W. Halley, S.J.A. Malham and A. Wiese:
Positive and implicit stochastic volatility simulation.
Preprint, 2009.
preprint
- S.L. Cotter, M. Dashti, J.C. Robinson and A.M. Stuart:
Bayesian inverse problems for functions and applications to fluid mechanics.
Inverse Problems, vol. 25, 2009.
online
- A. Beskos and A.M. Stuart:
MCMC methods for sampling function space.
Pages 337–364 in Proceedings of the 6th International Congress on Industrial and Applied Mathematicians (Zürich, 2007),
Rolf Jeltsch and Gerhard Wanner (editors), 2009.
- J.C. Mattingly, N.S. Pillai and A.M. Stuart:
SPDE Limits of the Random Walk Metropolis Algorithm in High Dimensions.
Preprint, 2009.
link
- A. Beskos, G.O. Roberts, A.M. Stuart and J. Voss:
MCMC Methods for Diffusion Bridges.
Stochastics and Dynamics, vol. 8, no. 3, pp. 319–350,
2008.
online, preprint, more…
- T. Müller-Gronbach and K. Ritter:
Minimal errors for strong and weak approximation of stochastic differential equations.
Pages 53–82 in Monte Carlo and Quasi-Monte Carlo Methods 2006,
A. Keller, S. Heinrich and H. Niederreiter (editors), 2008.
link
- M. Hairer, A.M. Stuart and J. Voss:
Analysis of SPDEs Arising in Path Sampling, Part II: The Nonlinear Case.
Annals of Applied Probability, vol. 17, no. 5,
pp. 1657–1706, 2007.
online, preprint, more…
- A. Apte, M. Hairer, A.M. Stuart and J. Voss:
Sampling The Posterior: An Approach to Non-Gaussian Data Assimilation.
Physica D: Nonlinear Phenomena, vol. 230, no. 1–2,
pp. 50–64, 2007.
online, preprint, more…
- A.M. Stuart, J. Voss and P. Wiberg:
Conditional Path Sampling of SDEs and the Langevin MCMC Method.
Communications in Mathematical Sciences, vol. 2, no. 4,
pp. 685–697, 2004.
link, preprint, more…