Details

Autor(en) / Beteiligte

• Buchner, Johannes

Titel

A statistical test for Nested Sampling algorithms

Ist Teil von

• Statistics and computing, 2016-01, Vol.26 (1-2), p.383-392

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2016

Link zum Volltext

Quelle

SpringerLink

Beschreibungen/Notizen

• 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.