Non-parametric estimation of Langevin equations (also called Stochastic Differential Equations or SDE) from a densely-observed 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 pseudo-inputs. The pseudo-inputs and the approximate posterior distributions are learnt using variational inference.
|Author||Constantino Antonio Garcia Martinez|
|Maintainer||Constantino Antonio Garcia Martinez <email@example.com>|
|License||GPL (>= 3)|
|Package repository||View on GitHub|
Install the latest version of this package by entering the following in R:
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.