modelOutputGrad: Compute derivatives with respect to params of model outputs.

In alkalait/gptk: Gaussian Processes Tool-Kit

Description

Compute derivatives with respect to params of model outputs.

Usage

  modelOutputGrad(model, X, dim)

Arguments

model	the model structure for which gradients are computed.
X	input locations where gradients are to be computed.
dim	the dimension of the model for which gradients are required.

Details

g <- modelOutputGrad(model, X) gives the gradients of the outputs from the model with respect to the parameters for a given set of inputs.

g <- modelOutputGrad(model, X, dim) gives the gradients of the outputs from the model with respect to the parameters for a given set of inputs.

Value

g	gradients of the model output with respect to the model parameters for the given input locations. The size of the returned matrix is of dimension number of data x number of parameters x number of model outputs (which maintains compatability with NETLAB).

See Also

modelLogLikelihood.

Examples

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alkalait/gptk documentation built on March 7, 2020, 6:30 a.m.