Use the shape-rate convention for the Gamma distribution. Define
The zero null space assumption makes a positive-definite matrix, and is the unique ordinary least squares solution. The Gaussian likelihood contributes , which must be retained because is unknown. Multiplying it by the improper prior and the Gamma distribution gives the normal-gamma posterior with a flat prior:
Here the constant density of the improper prior cancels on normalization. Put
The normal equation makes the residual orthogonal to the range of . The orthogonal decomposition by a closed subspace into that range and its orthogonal complement gives
Integration over by the Gaussian integral, followed by integration over using the Gamma distribution, yields
Thus the posterior distribution is proper, despite the improper prior; here follows from the zero null space assumption.
Completing the square in and collecting the powers of give the two conditional distributions:
Explicitly their probability density functions are
For a joint maximum a posteriori estimate relative to , first fix . The unique maximizing is . The remaining log density is plus a constant. Consequently, if ,
The stated assumptions do not always imply . If , the supremum occurs as but is not attained on ; if , the density is unbounded there. In either case there is no joint maximum a posteriori estimate in the stated parameter space. Calling zero a boundary mode does not make it an admissible positive precision.
If “MAP estimators” instead means separate modes of the marginal distributions, integration gives
Thus the marginal maximum a posteriori estimate of is always , and that of is when , with the same nonattainment at zero when . Joint and marginal maximization are different operations; the usual joint interpretation gives the preceding boxed pair.