Particle‐Based Bayesian State–Space Processors

[1]  E. Parzen.  Foundations of Time Series Analysis and Prediction Theory , 2002 .

[2]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[3]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[4]  G. Kitagawa.  A nonlinear smoothing method for time series analysis , 1991 .

[5]  BlakeAndrew,et al.  C ONDENSATION Conditional Density Propagation forVisual Tracking , 1998 .

[6]  G. Kitagawa.  Non-Gaussian State—Space Modeling of Nonstationary Time Series , 1987 .

[7]  Jun S. Liu,et al.  Rejection Control and Sequential Importance Sampling , 1998 .

[8]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[9]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[10]  H. Sorenson,et al.  Recursive bayesian estimation using gaussian sums , 1971 .

[11]  Petar M. Djuric,et al.  Guest editorial special issue on monte carlo methods for statistical signal processing , 2002, IEEE Trans. Signal Process..

[12]  H. Akaike.  A new look at the statistical model identification , 1974 .

[13]  Jim Q. Smith,et al.  Diagnostic checks of non‐standard time series models , 1985 .

[14]  Thomas B. Schön,et al.  Estimation of Nonlinear Dynamic Systems : Theory and Applications , 2006 .

[15]  Sylvia Früiiwirth-Schnatter,et al.  Recursive residuals and model diagnostics for normal and non-normal state space models , 1996, Environmental and Ecological Statistics.

[16]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[17]  D. M. Titterington,et al.  Improved Particle Filters and Smoothing , 2001, Sequential Monte Carlo Methods in Practice.

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

[19]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[20]  J.V. Candy,et al.  Bootstrap Particle Filtering , 2007, IEEE Signal Processing Magazine.

[21]  E. Ronchetti,et al.  Robust Inference for Generalized Linear Models , 2001 .

[22]  L. Shenton,et al.  Omnibus test contours for departures from normality based on √b1 and b2 , 1975 .

[23]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[24]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[25]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[26]  N. Gordon,et al.  Performance Issues in Non-Gaussian Filtering Problems , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

[27]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[28]  Xiaodong Wang,et al.  Monte Carlo methods for signal processing: a review in the statistical signal processing context , 2005, IEEE Signal Processing Magazine.

[29]  Fredrik Gustafsson,et al.  Adaptive Filtering and Change Detection: Gustafsson: Adaptive , 2001 .

[30]  Lang Hong,et al.  A comparison of nonlinear filtering approaches with an application to ground target tracking , 2005, Signal Process..

[31]  M. Rosenblatt.  Remarks on a Multivariate Transformation , 1952 .

[32]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[33]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[34]  Simon Haykin,et al.  Special Issue on Sequential State Estimation , 2004, Proc. IEEE.

[35]  Robert J. Elliott,et al.  Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature , 2007, Proceedings of the IEEE.

[36]  Simon J. Godsill,et al.  Particle methods for Bayesian modeling and enhancement of speech signals , 2002, IEEE Trans. Speech Audio Process..

[37]  Christophe Andrieu,et al.  Particle methods for change detection, system identification, and control , 2004, Proceedings of the IEEE.

[38]  Simon J. Godsill,et al.  An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.

[39]  Jun S. Liu,et al.  Blind Deconvolution via Sequential Imputations , 1995 .

[40]  Walter R. Gilks,et al.  RESAMPLE-MOVE Filtering with Cross-Model Jumps , 2001, Sequential Monte Carlo Methods in Practice.

[41]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[42]  R. Kohn,et al.  Diagnostics for Time Series Analysis , 1999 .

[43]  Nando de Freitas,et al.  Sequential Monte Carlo Methods for Neural Networks , 2001, Sequential Monte Carlo Methods in Practice.

[44]  R. Beran.  Minimum Hellinger distance estimates for parametric models , 1977 .

[45]  S. Frühwirth-Schnatter.  Applied state space modelling of non-Gaussian time series using integration-based Kalman filtering , 1994 .