Nonparametric estimation of Langevin equations (also called Stochastic Differential Equations or SDE) from a denselyobserved time series. estimate the drift and diffusion terms by modelling them as gaussian processes (GPs). To cope with the computational complexity that calculating the posterior distribution of the GPs requires, the GPs are approximated using a small set of function points, the inducing variables. These inducing variables are the result of evaluating the drift and diffusion terms at some strategically located pseudoinputs. The pseudoinputs and the approximate posterior distributions are learnt using variational inference.
Package details 


Author  Constantino Antonio Garcia Martinez 
Maintainer  Constantino Antonio Garcia Martinez <constantinoantonio.garcia@usc.es> 
License  GPL (>= 3) 
Version  0.1.0 
Package repository  View on GitHub 
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