For the model structure:
x \sim Gaussian(X beta,sigma^2)
sigma^2 \sim InvGamma(a,b)
beta \sim Gaussian(m,sigma^2 V)
Where X is a row vector, or a design matrix where each row is an obervation. InvGamma() is the Inverse-Gamma distribution, Gaussian() is the Gaussian distribution. See
dGaussian for the definitions of these distribution.
The model structure and prior parameters are stored in a "GaussianNIG" object.
Update (m,V,a,b) by adding the information of newly observed samples (x,X). The model structure and prior parameters are stored in a "GaussianNIG" object, the prior parameters in this object will be updated after running this function.
A "GaussianNIG" object.
Sufficient statistics of (x,X). In Gaussian-NIG case the sufficient statistic of sample (x,X) is a object of type "ssGaussianLinear", 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".
Banerjee, Sudipto. "Bayesian Linear Model: Gory Details." Downloaded from http://www. biostat. umn. edu/~ph7440 (2008).
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