Generate random samples from the posterior distribution of the following structure:
mu,Sigma|m,k,v,S \sim NIW(m,k,v,S)
x|mu,Sigma \sim Gaussian(mu,Sigma)
Where NIW() is the Normal-Inverse-Wishart distribution, Gaussian() is the Gaussian distribution. See
dGaussian for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIW" object.
Posterior distribution is NIW(mu,Sigma|m,k,v,S).
A "GaussianNIW" object.
Additional arguments to be passed to other inherited types.
list(mu,Sigma), where mu is a numeric vector, Sigma is a symmetric positive definite matrix.
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