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Biometrika 1995 82(2):339-350; doi:10.1093/biomet/82.2.339
© 1995 by Biometrika Trust
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The simulation smoother for time series models

PIET DE JONG and NEIL SHEPHARD

Faculty of Commerce and Business Administration, University of British Columbia Vancouver, B.C., V6T 1Z2, Canada
Nuffield College Oxford, OX1 1NF, U.K.

Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian time series.

Key Words: Gibbs sampling • Kalman filter • Simulation smoother • Smoothing • State space model


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