bbricks (version 0.1.4)

rPosterior.LinearGaussianGaussian: Posterior random generation of a "LinearGaussianGaussian" object

Description

Generate random samples from the posterior distribution of the following structure: $$x \sim Gaussian(A z + b, Sigma)$$ $$z \sim Gaussian(m,S)$$ Where Sigma is known. A is a \(dimx x dimz\) matrix, x is a \(dimx x 1\) random vector, z is a \(dimz x 1\) random vector, b is a \(dimm x 1\) vector. Gaussian() is the Gaussian distribution. See ?dGaussian for the definition of Gaussian distribution. The model structure and prior parameters are stored in a "LinearGaussianGaussian" object. Posterior distribution is Gaussian(z|m,S).

Usage

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

Arguments

obj

A "LinearGaussianGaussian" 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 z.

See Also

LinearGaussianGaussian, dPosterior.LinearGaussianGaussian

Examples

# NOT RUN {
obj <- LinearGaussianGaussian(gamma=list(Sigma=matrix(c(2,1,1,2),2,2),
                                         m=c(0.2,0.5,0.6),S=diag(3)))
rPosterior(obj = obj,n=20)
# }