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IndependentInverseGamma

class IndependentInverseGamma(α:Expression<Real>, β:Expression<Real[_]>) < Distribution<Real[_]>

Multivariate inverse-gamma distribution with independent components.

This is typically used to establish a conjugate prior for a Bayesian multivariate linear regression with number of outputs that are conditionally independent given the inputs:

where subscript denotes the (hyper)parameters of the th element of the output vector, are inputs, and are outputs.

The relationship is established in code as follows:

σ2:Random<Real[_]>;
α:Real;
β:Real[_];
W:Random<Real[_,_]>;
M:Real[_,_];
U:Real[_,_];
Y:Random<Real[_,_]>;
X:Real[_,_];

σ2 ~ InverseGamma(α, β);
W ~ Gaussian(M, U, σ2);
Y ~ Gaussian(W*X, σ2);

The advantage of using this approach over separate regressions is that expensive covariance operations are shared.

Member Variables

Name Description
α:Expression<Real> Shape.
β:Expression<Real[_]> Scales.