论文信息 - Bayesian filtering for hidden Markov models via Monte Carlo methods

Bayesian filtering for hidden Markov models via Monte Carlo methods

We propose a new Monte Carlo method for Bayesian filtering of general nonlinear and non-Gaussian hidden Markov models. This method is an extension of the well known importance sampling method. It is especially well-suited to sequential simulation as it allows one to split or kill trajectories according to a given score function. The model and estimation objectives are described. The new Monte Carlo method is presented. A few results on this method are established and its application to Bayesian filtering is described. Simulation results for several nonlinear and non-Gaussian time series are presented.

[1] Hisashi Tanizaki,et al. Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations , 1998 .

[2] Jun S. Liu,et al. Sequential Monte Carlo methods for dynamic systems , 1997 .

[3] Simon J. Godsill,et al. On sequential simulation-based methods for Bayesian filtering , 1998 .

[4] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

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

[6] G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[7] D. Mayne,et al. Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering† , 1969 .

[8] I. Lux. Monte Carlo Particle Transport Methods: Neutron and Photon Calculations , 1991 .