Monte Carlo Kalman filter and smoothing for multivariate discrete state space models

@inproceedings{Song2000MonteCK,
  title={Monte Carlo Kalman filter and smoothing for multivariate discrete state space models},
  author={Peter X. K. Song},
  year={2000}
}
The author studies state space models for multivariate binomial time series, focussing on the development of the Kalman filter and smoothing for state variables. He proposes a Monte Carlo approach employing the latent variable representation which transplants classical Kalman filter and smoothing developed for Gaussian state space models to discrete models and leads to a conceptually simple and computationally convenient approach. The method is illustrated through simulations and concrete… CONTINUE READING