B2GaussianDistribution: A Gaussian Process distribution for probabilities

In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

A Gaussian Process distribution for probabilities

Description

A Gaussian Process distribution for probabilities

Simulate a Gaussian Distribution on B2

Usage

B2GaussianDistribution(
  mean = BDataLogDensities(list(function(x) rep(0, length(x))), basis = B2Unif01Basis7),
  var = varKernel(function(x, y) exp(-abs(outer(x, y, "-"))), basis =
    getBayesSpaceBasis(mean)),
  nEval = 513
)

## S3 method for class 'B2GaussianDistribution'
simulate(object, nsim = 1, seed = NULL, ..., nEval = object$nEval)

Arguments

mean	something covertable to a single BDataLogDensity, providing the Bayes space expectation of the distribution, as well as BayesSpace and Support. Currently only finite continuous supports are possible. You might want to simulate on B2Unif01 and transform for other Transformed spaces.
var	a function of two arguments providing the covariance operator
nEval	the number of equispaced discretization points used on the interval.
object	the B2GaussianDistribution object
nsim	the number of simulations to be computed
seed	the optional seed, if used it will be reported as a seed attribution of the result (use with care)
...	unused arguments for generic use

Value

B2GaussianDistribution: a B2GaussianDistribution object which can be simulated with, simulate: BDataLogDensities-Object with nsim cases with the same BayesSpace and BayesSpaceBasis as the mean.

Methods (by generic)

• simulate(B2GaussianDistribution): Simulaton method

See Also

[

[stats::simulate]

Examples

m = BDataPDFs(list(dunif),basis=B2Unif01Basis7)
G1 = B2GaussianDistribution(m,function(x,y) exp(-(4*abs(x-y)^1.7)))
plot(simulate(G1,10,seed=47),col=2:11)

GD = B2GaussianDistribution(mean=BDataPDFs(\(x)x,basis=B2Unif01Basis5),
                            var=\(x,y) exp(-abs(x-y)))
plot(simulate(GD,5))
GD = B2GaussianDistribution(mean=BDataPDFs(\(x) dnorm(x),basis=B2N01Basis5),
                            var=\(x,y) exp(-abs(x-y)))
plot(as.BDataPDFs(simulate(GD,5)))

DistributionalDDA documentation built on Oct. 20, 2026, 5:06 p.m.