Inference for nonlinear dynamical systems

@article{Ionides2006InferenceFN,
  title={Inference for nonlinear dynamical systems},
  author={E. Ionides and C. Bret{\'o} and A. King},
  journal={Proceedings of the National Academy of Sciences},
  year={2006},
  volume={103},
  pages={18438 - 18443}
}
Nonlinear stochastic dynamical systems are widely used to model systems across the sciences and engineering. Such models are natural to formulate and can be analyzed mathematically and numerically. However, difficulties associated with inference from time-series data about unknown parameters in these models have been a constraint on their application. We present a new method that makes maximum likelihood estimation feasible for partially-observed nonlinear stochastic dynamical systems (also… Expand
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