Abstract
This article looks at Skilling's nested sampling from a physical perspective
and interprets it as a microcanonical demon algorithm. Using key
quantities of statistical physics we investigate the performance
of nested sampling on complex systems such as Ising, Potts and protein
models. We show that releasing multiple demons helps to smooth the
truncated prior and eases sampling from it because the demons keep
the particle off the constraint boundary. For continuous systems
it is straightforward to extend this approach and formulate a phase
space version of nested sampling that benefits from correlated explorations
guided by Hamiltonian dynamics.
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