Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

@article{Schn2017ProbabilisticLO,
  title={Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo},
  author={Thomas B. Sch{\"o}n and Andreas Svensson and Lawrence Murray and Fredrik Lindsten},
  journal={CoRR},
  year={2017},
  volume={abs/1703.02419}
}
Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to… CONTINUE READING