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

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

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 class '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

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)

bbricks documentation built on July 8, 2020, 7:29 p.m.