@book{VanLeeuwenChengReich2015,
  author    = {Van Leeuwen, Peter Jan and Cheng, Yuan and Reich, Sebastian},
  title     = {Nonlinear data assimilation},
  series = {Frontiers in applied dynamical systems: reviews and tutorials ; 2},
  journal   = {Frontiers in applied dynamical systems: reviews and tutorials ; 2},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-18346-6},
  doi       = {10.1007/978-3-319-18347-3},
  pages     = {xii, 118},
  year      = {2015},
  abstract  = {This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.},
  language  = {en}
}
@phdthesis{Cheng2016,
  author    = {Cheng, Yuan},
  title     = {Recursive state estimation in dynamical systems},
  school      = {Universit{\"a}t Potsdam},
  pages     = {84},
  year      = {2016},
  language  = {en}
}
@article{ChengLenkoshi2012,
  author    = {Cheng, Yuan and Lenkoshi, Alex},
  title     = {Hierarchical gaussian graphical models beyond reversible jump},
  series = {Electronic journal of statistics},
  volume    = {6},
  journal   = {Electronic journal of statistics},
  publisher = {Institute of Mathematical Statistics},
  address   = {Cleveland},
  issn      = {1935-7524},
  doi       = {10.1214/12-EJS746},
  pages     = {2309 -- 2331},
  year      = {2012},
  abstract  = {The Gaussian Graphical Model (GGM) is a popular tool for incorporating sparsity into joint multivariate distributions. The G-Wishart distribution, a conjugate prior for precision matrices satisfying general GGM constraints, has now been in existence for over a decade. However, due to the lack of a direct sampler, its use has been limited in hierarchical Bayesian contexts, relegating mixing over the class of GGMs mostly to situations involving standard Gaussian likelihoods. Recent work has developed methods that couple model and parameter moves, first through reversible jump methods and later by direct evaluation of conditional Bayes factors and subsequent resampling. Further, methods for avoiding prior normalizing constant calculations-a serious bottleneck and source of numerical instability-have been proposed. We review and clarify these developments and then propose a new methodology for GGM comparison that blends many recent themes. Theoretical developments and computational timing experiments reveal an algorithm that has limited computational demands and dramatically improves on computing times of existing methods. We conclude by developing a parsimonious multivariate stochastic volatility model that embeds GGM uncertainty in a larger hierarchical framework. The method is shown to be capable of adapting to swings in market volatility, offering improved calibration of predictive distributions.},
  language  = {en}
}