... In its fully developed form (circa 1993) it is a fully Bayesian implementation, using conjugate gradient approximations to evaluate and provide error bounds on the all-important "Evidence" (which can be used to compare different prior models). ... Nested Sampling . ... Nested Sampling & Conjugate Gradient . ... Here are John Skilling's latest thoughts on how you can apply all the conjugate gradient technology from MemSys to find that elusive new sample: nested_conjgrad.pdf . ...