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Approximate Gaussian process inference for the drift function in stochastic differential equations
Andreas Ruttor · Philipp Batz · Manfred Opper

Thu Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None

We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from incomplete observations of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobserved, latent dynamics between observations. The posterior over states is approximated by a piecewise linearized process and the MAP estimation of the drift is facilitated by a sparse Gaussian process regression.

Author Information

Andreas Ruttor (TU Berlin)
Philipp Batz (TU Berlin)
Manfred Opper (TU Berlin)

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