sigmoidVarKernGradient

kern

covGrad

<p>Compute gradient of sigmoid kernel with respect to each parameter (used by gptk)</p>

Efficient Bayesian parameter inference for systems of ordinary
differential equations. The inference is based on adaptive gradient matching
(AGM, Dondelinger et al. 2013 <http://proceedings.mlr.press/v31/dondelinger13a.pdf>,
Macdonald 2017 <http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf>),
which offers orders-of-magnitude improvements in computational
efficiency over standard methods that require solving the differential
equation system. Features of the package include flexible specification
of custom ODE systems as R functions, support for missing variables,
Bayesian inference via population MCMC.

Frank Dondelinger

deGradInfer

Parameter Inference for Systems of Differential Equation

Benn Macdonald

sigmoidVarKernGradient function

Compute gradient of sigmoid kernel with respect to each parameter (used by gptk) — sigmoidVarKernGradient

Compute gradient of sigmoid kernel with respect to each parameter (used by gptk)

sigmoidVarKernGradient: Compute gradient of sigmoid kernel with respect to each parameter (used by gptk)

Description

Usage

Arguments

Value