rPosterior.LinearGaussianGaussian {bbricks} | R Documentation |
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).
## S3 method for class 'LinearGaussianGaussian' rPosterior(obj, n = 1, ...)
obj |
A "LinearGaussianGaussian" object. |
n |
integer, number of samples. |
... |
Additional arguments to be passed to other inherited types. |
A matrix of n rows, each row is a sample of z.
LinearGaussianGaussian
, dPosterior.LinearGaussianGaussian
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)