Sequential Monte Carlo Methods
- An overview of existing methods and recent advances in sequential Monte Carlo by Cappe et al. (2007)
- A Survey of sequential Monte Carlo methods for economics and finance by Creal (2012): An excellent review of SMC methods, with an up to date overview of different methods and theoretical results
- An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models by Kantas et al. (2009): An excellent review of offline and online methods for parameter estimation
- Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models by Gao & Zhang (2012): Good example of PMCMC methods
Extented Sampling on SMC methods
- Efficient Block Sampling Strategies for SMC methods by Doucet et al. 2006.
- A note on the use of Metropolis Hastings Kernels in Importance Sampling by Del Moral, Doucet & Jarsa
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.