Nested sampling is an iterative integration procedure that shrinks the prior volume towards higher likelihoods by removing a “live” point at a time. A replacement point is drawn uniformly from the prior above an ever-increasing likelihood threshold. Thus, the problem of drawing from a space above a certain likelihood value arises naturally in nested sampling, making algorithms that solve this problem a key ingredient to the nested sampling framework. If the drawn points are distributed uniformly, the removal of a point shrinks the volume in a well-understood way, and the integration of nested sampling is unbiased. In this work, I develop a statistical test to check whether this is the case. This “Shrinkage Test” is useful to verify nested sampling algorithms in a controlled environment. I apply the shrinkage test to a test-problem, and show that some existing algorithms fail to pass it due to over-optimisation. I then demonstrate that a simple algorithm can be constructed which is robust against this type of problem. This RADFRIENDS algorithm is, however, inefficient in comparison to MULTINEST.
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Skilling (2004) uses the estimator \(\left\langle \ln S\right\rangle =-1/N\), which is better behaved at small N. For this introduction the simpler, intuitive formula is sufficient.
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I would like to thank Frederik Beaujean and Udo von Toussaint for reading the initial manuscript. I acknowledge funding through a doctoral stipend by the Max Planck Society. This manuscript has greatly benefited from the comments of the two anonymous referees, whom I would also like to thank. I acknowledge financial support through a Max Planck society stipend.
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Buchner, J. A statistical test for Nested Sampling algorithms. Stat Comput 26, 383–392 (2016). https://doi.org/10.1007/s11222-014-9512-y
- Nested sampling
- Bayesian inference
- Marginal likelihood