Approximate Methods for State-Space Models

@article{Koyama2010ApproximateMF,
  title={Approximate Methods for State-Space Models},
  author={Shinsuke Koyama and Lucia Castellanos P{\'e}rez-Bolde and C. Shalizi and R. Kass},
  journal={Journal of the American Statistical Association},
  year={2010},
  volume={105},
  pages={170 - 180}
}
  • Shinsuke Koyama, Lucia Castellanos Pérez-Bolde, +1 author R. Kass
  • Published 2010
  • Mathematics, Physics, Biology, Medicine
  • Journal of the American Statistical Association
  • State-space models provide an important body of techniques for analyzing time series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models… CONTINUE READING
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