Comparison of Step Samplers for Nested Sampling

Understand

Bayesian inference with nested sampling requires a likelihood-restricted prior sampling method, which draws samples from the prior distribution that exceed a likelihood threshold.

• For high-dimensional problems, Markov Chain Monte Carlo derivatives have been proposed.
• We numerically study ten algorithms based on slice sampling, hit-and-run and differential evolution algorithms in ellipsoidal, non-ellipsoidal and non-convex problems from 2 to 100 dimensions.
• Mixing capabilities are evaluated with the nested sampling shrinkage test.