bbricks (version 0.1.4)

dPosterior.LinearGaussianGaussian: Posterior density function of a "LinearGaussianGaussian" object

Description

Generate the the density value of 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 density is the density function of Gaussian(z|m,S).

Usage

# S3 method for LinearGaussianGaussian
dPosterior(obj, z, LOG = TRUE, ...)

Arguments

obj

A "LinearGaussianGaussian" object.

z

matrix, or the ones that can be converted to matrix. Each row of z is an sample.

LOG

Return the log density if set to "TRUE".

...

Additional arguments to be passed to other inherited types.

Value

A numeric vector of the same length as nrow(z), the posterior density.

See Also

LinearGaussianGaussian, rPosterior.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)))
z <- rGaussian(10,mu = runif(3),Sigma = diag(3))
dPosterior(obj = obj,z=z)
dPosterior(obj = obj,z=z,LOG=FALSE)
# }