xed-lag smoothing in state-space models

[1]  É. Mémin,et al.  Weighted ensemble transform Kalman filter for image assimilation , 2013 .

[2]  Ying Sun,et al.  Geostatistics for Large Datasets , 2012 .

[3]  G. Roberts,et al.  Exact simulation of diffusions , 2005, math/0602523.

[4]  A. Doucet,et al.  Monte Carlo Smoothing for Nonlinear Time Series , 2004, Journal of the American Statistical Association.

[5]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[6]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[7]  P. Fearnhead,et al.  Particle filters for partially observed diffusions , 2007, 0710.4245.

[8]  P. Fearnhead,et al.  Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) , 2006 .

[9]  G. Evensen,et al.  An ensemble Kalman smoother for nonlinear dynamics , 2000 .

[10]  Jonathan R. Stroud,et al.  An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation , 2010 .

[11]  A. Doucet,et al.  Smoothing algorithms for state–space models , 2010 .

[12]  P. Leeuwen,et al.  Nonlinear data assimilation in geosciences: an extremely efficient particle filter , 2010 .

[13]  Peter Jan,et al.  Particle Filtering in Geophysical Systems , 2009 .

[14]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[15]  P. Bickel,et al.  Obstacles to High-Dimensional Particle Filtering , 2008 .

[16]  Pierre Del Moral,et al.  Feynman-Kac formulae , 2004 .

[17]  A. Gallant,et al.  Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes , 2002 .

[18]  J.M.C. Clark The simulation of pinned diffusions , 1990, 29th IEEE Conference on Decision and Control.

[19]  P. Protter,et al.  The Monte-Carlo method for filtering with discrete-time observations , 2001 .