A Defensive Marginal Particle Filtering Method for Data Assimilation
暂无分享,去创建一个
[1] Dongbin Xiu,et al. A generalized polynomial chaos based ensemble Kalman filter with high accuracy , 2009, J. Comput. Phys..
[2] Guang Lin,et al. An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling , 2014, J. Comput. Phys..
[3] Yan Zhou,et al. Multilevel Particle Filters , 2015, SIAM J. Numer. Anal..
[4] Leslie Greengard,et al. A fast algorithm for particle simulations , 1987 .
[5] T. Hesterberg,et al. Weighted Average Importance Sampling and Defensive Mixture Distributions , 1995 .
[6] N. Papadakis,et al. Data assimilation with the weighted ensemble Kalman filter , 2010 .
[7] M. Ghil,et al. Data assimilation in meteorology and oceanography , 1991 .
[8] Matthias Morzfeld,et al. What the collapse of the ensemble Kalman filter tells us about particle filters , 2015, 1512.03720.
[9] A. Doucet,et al. A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .
[10] Jean-Marie Cornuet,et al. Adaptive Multiple Importance Sampling , 2009, 0907.1254.
[11] G. Evensen. Data Assimilation: The Ensemble Kalman Filter , 2006 .
[12] Arnaud Doucet,et al. Particle methods for optimal filter derivative: application to parameter estimation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[13] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[14] Florian Nadel,et al. Stochastic Processes And Filtering Theory , 2016 .
[15] Nando de Freitas,et al. Toward Practical N2 Monte Carlo: the Marginal Particle Filter , 2005, UAI.
[16] Sebastian Thrun,et al. Probabilistic robotics , 2002, CACM.
[17] K. Ide,et al. A Method for Assimilation of Lagrangian Data , 2003 .
[18] Jun S. Liu,et al. Mixture Kalman filters , 2000 .
[19] K. Ide,et al. A Method for Assimilating Lagrangian Data into a Shallow-Water-Equation Ocean Model , 2006 .
[20] Nando de Freitas,et al. Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.
[21] Tiangang Cui,et al. On dimension reduction in Gaussian filters , 2015, 1508.06452.
[22] D. Nychka. Data Assimilation” , 2006 .
[23] Paul Zarchan,et al. Fundamentals of Kalman Filtering: A Practical Approach , 2001 .
[24] A. Stordal,et al. Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter , 2011 .
[25] Leonidas J. Guibas,et al. Optimally combining sampling techniques for Monte Carlo rendering , 1995, SIGGRAPH.
[26] Chris Snyder,et al. Toward a nonlinear ensemble filter for high‐dimensional systems , 2003 .
[27] A. Stuart,et al. Sampling the posterior: An approach to non-Gaussian data assimilation , 2007 .
[28] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[29] E. Lorenz. Deterministic nonperiodic flow , 1963 .
[30] J. L. Roux. An Introduction to the Kalman Filter , 2003 .
[31] H. Kunsch,et al. Bridging the ensemble Kalman and particle filters , 2012, 1208.0463.
[32] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[33] Simon J. Godsill,et al. An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.
[34] A. Owen,et al. Safe and Effective Importance Sampling , 2000 .