  Radial basis functions are a common mathematical tool used to construct a
smooth interpolating function from a set of data points. A spatial prior based
on thin-plate spline radial basis functions can be easily implemented resulting
in a posterior that can be sampled directly using Monte Carlo integration,
avoiding the computational burden and potential inefficiency of an Monte Carlo
Markov Chain (MCMC) sampling scheme. The derivation of the prior and sampling
scheme are demonstrated.
