bbricks (version 0.1.4)

rPosterior.GaussianGaussian: Generate ramdom samples from the posterior distribution of a "GaussianGaussian" object

Description

Generate random samples from the posterior distribution of the following structure: $$x \sim Gaussian(mu,Sigma)$$ $$mu \sim Gaussian(m,S)$$ Where Sigma is known. Gaussian() is the Gaussian distribution. See ?dGaussian for the definition of Gaussian distribution. The model structure and prior parameters are stored in a "GaussianGaussian" object. Posterior distribution is Gaussian(mu|m,S).

Usage

# S3 method for GaussianGaussian
rPosterior(obj, n = 1, ...)

Arguments

obj

A "GaussianGaussian" object.

n

integer, number of samples.

...

Additional arguments to be passed to other inherited types.

Value

A matrix of n rows, each row is a sample of mu.

See Also

GaussianGaussian, dPosterior.GaussianGaussian

Examples

# NOT RUN {
obj <- GaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2),m=c(0.2,0.5),S=diag(2)))
rPosterior(obj=obj,n=20)
# }