Inference for nonlinear dynamical systems

  title={Inference for nonlinear dynamical systems},
  author={Edward L. Ionides and Carles Bret{\'o} and Aaron A. King},
  journal={Proceedings of the National Academy of Sciences},
  pages={18438 - 18443}
  • E. IonidesC. BretóA. King
  • Published 5 December 2006
  • Mathematics, Computer Science
  • Proceedings of the National Academy of Sciences
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… 

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