In this article we revisit the auxiliary variable method introduced in Smith
and kohn (1996) for the fitting of P-th order spline regression models with an
unknown number of knot points. We introduce modifications which allow the
location of knot points to be random, and we further consider an extension of
the method to handle models with non-Gaussian errors. We provide a new
algorithm for the MCMC sampling of such models. Simulated data examples are
used to compare the performance of our method with existing ones.