Published December 2004
| Published
Journal Article
Open
Conditional Path Sampling of SDEs and the Langevin MCMC Method
- Creators
-
Stuart, Andrew M.
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Voss, Jochen
- Wilberg, Petter
Chicago
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Abstract
We introduce a stochastic PDE based approach to sampling paths of SDEs, conditional on observations. The SPDEs are derived by generalising the Langevin MCMC method to infinite dimensions. Various applications are described, including sampling paths subject to two end-point conditions (bridges) and nonlinear filter/smoothers.
Additional Information
© 2004 International Press. Received: July 14, 2004; accepted (in revised version): October 7, 2004. We are grateful to Eric Vanden Eijnden for helpful suggestions. This project was partially funded by the EPSRC. Jochen Voss was additionally funded by the European Union with a Marie Curie scholarship. The computing facilities were provided by the Centre for Scientific Computing of the University of Warwick.Attached Files
Published - euclid.cms.1109885503.pdf
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euclid.cms.1109885503.pdf
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Additional details
- Eprint ID
- 78125
- Resolver ID
- CaltechAUTHORS:20170612-144819276
- Engineering and Physical Sciences Research Council (EPSRC)
- Marie Curie Fellowship
- Created
-
2017-06-12Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field
- Other Numbering System Name
- Andrew Stuart
- Other Numbering System Identifier
- J61