%0 Journal Article %1 Godsill:1March2004:0162-1459:156 %A Godsill, Simon J. %A Doucet, Arnaud %A West, Mike %D 2004 %J Journal of the American Statistical Association %K Bayes assimilation bayes inference inversions mcmc models statistics uncertainty %P 156-168(13) %R doi:10.1198/016214504000000151 %T Monte Carlo Smoothing for Nonlinear Time Series %U http://www.ingentaconnect.com/content/asa/jasa/2004/00000099/00000465/art00016 %V 99 %X We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filtered trajectories.

@article{Godsill:1March2004:0162-1459:156, abstract = {We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filtered trajectories.}, added-at = {2008-09-10T16:38:50.000+0200}, author = {Godsill, Simon J. and Doucet, Arnaud and West, Mike}, biburl = {https://www.bibsonomy.org/bibtex/2606dd28d25139567a53c37fc88e1c7be/jgomezdans}, description = {IngentaConnect Monte Carlo Smoothing for Nonlinear Time Series}, doi = {doi:10.1198/016214504000000151}, interhash = {cc553b685ac69bfd54c19d6fb7efa5f6}, intrahash = {606dd28d25139567a53c37fc88e1c7be}, journal = {Journal of the American Statistical Association}, keywords = {Bayes assimilation bayes inference inversions mcmc models statistics uncertainty}, month = mar, pages = {156-168(13)}, timestamp = {2008-09-10T16:38:50.000+0200}, title = {Monte Carlo Smoothing for Nonlinear Time Series}, url = {http://www.ingentaconnect.com/content/asa/jasa/2004/00000099/00000465/art00016}, volume = 99, year = { 2004} }