For the model 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.
Update (m,k,v,S) by adding the information of newly observed samples x. The model structure and prior parameters are stored in a "GaussianNIW" object, the prior parameters in this object will be updated after running this function.
A "GaussianNIW" object.
Sufficient statistics of x. In Gaussian-NIW case the sufficient statistic of sample x is a object of type "ssGaussian", it can be generated by the function sufficientStatistics().
Additional arguments to be passed to other inherited types.
None. the gamma stored in "obj" will be updated based on "ss".
Murphy, Kevin P. "Conjugate Bayesian analysis of the Gaussian distribution." def 1.22 (2007): 16.
Gelman, Andrew, et al. "Bayesian Data Analysis Chapman & Hall." CRC Texts in Statistical Science (2004).
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