 Normal regression with independent coefficient and variance priors

In a normal linear model $Y\mid b,s\sim N(Xb,sI)$, assign the coefficient vector $b$ a proper normal distribution with fixed positive-definite covariance matrix $D$, independently of the variance $s$, whose prior distribution is inverse-gamma distribution. Completing the square givesMultiplying the residual likelihood function by the inverse-gamma distribution density also givesThese full conditional distributions permit a Gibbs sampler. The marginal coefficient posterior distribution is obtained by marginalization over $s$. Its conditional mean and covariance matrix both depend on $s$, so it is generally not a Student's t-distribution. Even if the columns of $X$ and the prior coordinates make the conditional coefficient components independent, averaging over a shared unknown $s$ need not preserve that independence.

A normal-inverse-gamma prior instead scales the conditional coefficient prior covariance matrix by $s$, producing a multivariate Student's t-distribution on marginalization. It is a different joint prior distribution.