Sequential Monte Carlo Methods

Reviews:

 

Extented Sampling on SMC methods

 

High Dimension Paticle Filtering

  • An Improved Data Assimilation Scheme for High Dimensional Nonlinear Systems by Monajemi and Kitanidis (2012)
  • Sequential state and parameter estimation using combined ensemble Kalman and particle filter updates Künsch & Frial (2012)
  • Ensemble Filtering for High Dimensional Non-linear State Space Models by Lei and Bickel (2012)
  • A Framework for Data Assimilation and Forecasting in High-Dimensional Non-Linear Dynamical Systems by Bengtsson, Nychka, Snyder (2012)
  • Curse of Dimensionality Revisited: the Collapse of Importance Sampling in Very Large Scale Systems by Bickel (2008)
  • Nonlinear data assimilation in geosciences: an extremely efficient particle filter by  P. J. van Leeuwen (2010)
  • A new particle filter for high-dimensional state-space models based on intensive and extensive proposal distribution by Nguyen et al. (2010)
  • Approximate importance sampling Monte Carlo for data assimilation by Berliner & Wikle (2007)
  • Merging particle filter for sequential data assimilation by  Nakano et al. (2007)
  • On the Stability of Sequential Monte Carlo Methods in High Dimensions, by Beskos et al. (2011)

 

SMC combined with MCMC

  • Particle MCMC Methods. Holenstein's Doctoral Thesis, University of Bonn, 2009.
  • Non-asymptotic Error Bounds for Sequential MCMC Methods, Doctoral Thesis, University of Bonn, 2011.

 

Links

Doucet's website on SMC resources

Del Moral's website on SMC papers

Workshop on SMC in Warwick September 2012

Workshop on Confronting Intractability in Statistical Inference in Bristol April 2012

 

Published Aug. 22, 2012 8:42 AM - Last modified Oct. 17, 2014 1:52 PM