## Smoothing algorithms for state-space models (2004)

Venue: | in Submission IEEE Transactions on Signal Processing |

Citations: | 30 - 4 self |

### BibTeX

@TECHREPORT{Briers04smoothingalgorithms,

author = {Mark Briers and Arnaud Doucet and Simon Maskell},

title = {Smoothing algorithms for state-space models},

institution = {in Submission IEEE Transactions on Signal Processing},

year = {2004}

}

### Years of Citing Articles

### OpenURL

### Abstract

A prevalent problem in statistical signal processing, applied statistics, and time series analysis is the calculation of the smoothed posterior distribution, which describes the uncertainty associated with a state, or a sequence of states, conditional on data from the past, the present, and the future. The aim of this paper is to provide a rigorous foundation for the calculation, or approximation, of such smoothed distributions, to facilitate a robust and efficient implementation. Through a cohesive and generic exposition of the scientific literature we offer several novel extensions such that one can perform smoothing in the most general case. Experimental results for: a Jump Markov Linear System; a comparison of particle smoothing methods; and parameter estimation using a particle implementation of the EM algorithm, are provided.