Description

A matrix-normal prior distribution, intended as the conjugate prior for the regression coefficients in a multivariate linear regression.

Usage

Arguments

Details

The matrix normal distribution is a 3-parameter distribution MN(mu, Omega, V), where mu is the mean. A deviate from the distribution is a matrix B, where Cov(B[i, j], B[k, m]) = Omega[i, k] * Sigma[j, m]. If b = Vec(B) is the vector obtained by stacking columns of B, then b is multivariate normal with mean Vec(mu) and covariance matrix

Sigma %x% Omega

(the kronecker product).

This prior distribution assumes the underlying C++ code knows where to find the predictor (X) matrix in the regression, and the residual variance matrix Sigma. The assumed prior distribution is B ~ MN(mu, X'X / nu, Sigma).

Like most other priors in Boom, this function merely encodes information expected by the underlying C++ code, ensuring correct names and formatting.

Author(s)

Steven L. Scott steve.the.bayesian@gmail.com