doit_marginals: DoIt approximation of all marginal densities

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

Description

Approximate the marginal densities of all elements of the vector 'theta'.

Usage

doit_marginals(doit, theta_eval = NULL)

Arguments

doit	An object of class 'doit', see function 'doit_fit'.
theta_eval	Evaluation points at which to approximate the marginal distribution. If 'NULL' (the default) the original design points are used.

Value

A data frame of the provided 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)
mar      = doit_marginals(fit)

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