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).
A "LinearGaussianGaussian" object.
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.
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