Generate the MAP estimate of (beta,sigma^2) in following Gaussian-NIG 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.
The MAP estimates are:
(beta,sigma^2)_MAP = argmax p(beta,sigma^2|m,V,a,b,x,X)
A "GaussianNIG" object.
Additional arguments to be passed to other inherited types.
A named list, the MAP estimate of beta and sigma^2.
Banerjee, Sudipto. "Bayesian Linear Model: Gory Details." Downloaded from http://www. biostat. umn. edu/~ph7440 (2008).
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