Approximate Bayes learning of stochastic differential equations.

@article{Batz2018ApproximateBL,
  title={Approximate Bayes learning of stochastic differential equations.},
  author={Philipp Batz and Andreas Ruttor and M. Opper},
  journal={Physical review. E},
  year={2018},
  volume={98 2-1},
  pages={
          022109
        }
}
We introduce a nonparametric approach for estimating drift and diffusion functions in systems of stochastic differential equations from observations of the state vector. Gaussian processes are used as flexible models for these functions, and estimates are calculated directly from dense data sets using Gaussian process regression. We develop an approximate expectation maximization algorithm to deal with the unobserved, latent dynamics between sparse observations. The posterior over states is… Expand
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