doit_marginal_A: DoIt approximation of the marginal of a linear transformation

In sieste/doit: Bayesian Computation using Design of Experiments-Based Interpolation Technique

Description

Approximate the marginal density of a linear transformation 'A

Usage

doit_marginal_A(doit, A = NULL, theta_eval = NULL)

Arguments

doit	An object of class 'doit', see function 'doit_fit'.
A	The transformation matrix.
theta_eval	Evaluation points at which to apply the linear transformation and then approximate the marginal distribution. If 'NULL' (the default) the original design points are used.

Value

A data frame of the transformed evaluation points and the corresponding DoIt approximation of the marginal density.

Examples

design = data.frame(x=rnorm(10), y=rnorm(10))
design$f = with(design, exp(-0.5*(x+y)^2))
fit = doit_fit(design)
# distribution of x + y
mar_xpy = doit_marginal_A(fit, A=matrix(c(1,1), 1, 2))

sieste/doit documentation built on May 9, 2019, 4:10 p.m.