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Monte Carlo Fixed-lag Smoothing in State-space Models : Volume 21, Issue 3 (28/05/2014)

By Cuzol, A.

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Book Id: WPLBN0003992567
Format Type: PDF Article :
File Size: Pages 11
Reproduction Date: 2015

Title: Monte Carlo Fixed-lag Smoothing in State-space Models : Volume 21, Issue 3 (28/05/2014)  
Author: Cuzol, A.
Volume: Vol. 21, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Mémin, E., & Cuzol, A. (2014). Monte Carlo Fixed-lag Smoothing in State-space Models : Volume 21, Issue 3 (28/05/2014). Retrieved from http://worldebookfair.org/


Description
Description: University of Bretagne-Sud, UMR 6205, LMBA, 56000 Vannes, France. This paper presents an algorithm for Monte Carlo fixed-lag smoothing in state-space models defined by a diffusion process observed through noisy discrete-time measurements. Based on a particle approximation of the filtering and smoothing distributions, the method relies on a simulation technique of conditioned diffusions. The proposed sequential smoother can be applied to general nonlinear and multidimensional models, like the ones used in environmental applications. The smoothing of a turbulent flow in a high-dimensional context is given as a practical example.

Summary
Monte Carlo fixed-lag smoothing in state-space models

Excerpt
Beskos, A. and Roberts, G. O.: Exact simulation of diffusions, The Annal. Appl. Probabil., 15, 2422–2444, 2005.; Beskos, A., Papaspiliopoulos, O., Roberts, G. O., and Fearnhead, P.: Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion), J. Roy. Stat. Soc. B, 68, 333–382, 2006.; Beyou, S., Cuzol, A., Gorthi, S., and Mémin, E.: Weighted Ensemble Transform Kalman Filter for Image Assimilation, Tellus A, 65, 18803, doi:10.3402/tellusa.v65i0.18803, 2013.; Briers, M., Doucet, A., and Maskell, S.: Smoothing algorithms for state-space models, Ann. Insti. Stat. Mathe., 62, 61–89, 2010.; Clark, J.: The simulation of pinned diffusions, in: Proceedings of the 29th IEEE Conference on Decision and Control, 1418–1420, 1990.; Del Moral, P.: Feynman-Kac Formulae. Genealogical and Interacting Particle Systems with Applications, Springer, 2004.; Del Moral, P., Jacod, J., and Protter, P.: The Monte Carlo Method for filtering with discrete-time observations, Probabil. Theor. Relat. Fields, 120, 346–368, 2001.; Delyon, B. and Hu, Y.: Simulation of conditioned diffusions and applications to parameter estimation, Stochast. Process. Applic., 116, 1660–1675, 2006.; Doucet, A., Godsill, S., and Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering, Stat. Comput., 10, 197–208, 2000.; Durham, G. and Gallant, A.: Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes, J. Business Econom. Stat., 20, 297–316, 2002.; Evensen, G.: The ensemble Kalman filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003.; Evensen, G. and van Leeuwen, P.: An ensemble Kalman Smoother for nonlinear dynamics, Mon. Weather Rev., 128, 1852–1867, 2000.; Fearnhead, P., Papaspiliopoulos, O., and Roberts, G.: Particle filters for partially observed diffusions, J. Roy. Stat. Soc. B, 70, 755–777, 2008.; Godsill, S. J., Doucet, A., and West, M.: Monte Carlo smoothing for nonlinear time series, J. Am. Stat. Assoc., 99, 156–168, 2004.; Gordon, N., Salmond, D., and Smith, A.: Novel approach to non-linear/non-Gaussian Bayesian state estimation, IEEE Proc. F, 140, 107–113, 1993.; Papadakis, N., Mémin, E., Cuzol, A., and Gengembre, N.: Data assimilation with the weighted ensemble Kalman filter, Tellus A: Dynam. Meteorol. Oceanogr., 62, 673–697, 2010.; Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640, 2008.; Stroud, J. R., Stein, M. L., Lesht, B. M., Schwab, D. J., and Beletsky, D.: An ensemble Kalman filter and smoother for satellite data assimilation, J. Am. Stat. Assoc., 105, 978–990, 2010.; Sun, Y., Bo, L., and Genton, M. G.: Geostatistics for large datasets, in: Advances and challenges in space-time modelling of natural events, 55–77, Springer, 2012.; Van Leeuwen, P. J.: Particle filtering in Geophysical systems, Mon. Weather Rev., 137, 4089–4114, 2009.; Van Leeuwen, P. J.: Nonlinear data assimilation in geosciences: an extremely efficient particle filter, Q. J. Roy. Meteorol. Soc., 136, 1991–1999, 2010.; Van Leeuwen, P. J. and Ades, M.: Efficient fully nonlinear data assimilation for geophysical fluid dynamics, Comput. Geosci., 55, 16–27, 2013.

 

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