Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11222-014-9512-y | Statistics and Computing |
Keywords | Field | DocType |
Nested sampling, MCMC, Bayesian inference, Evidence, Test, Marginal likelihood | Nested sampling algorithm,Bayesian inference,Markov chain Monte Carlo,Algorithm,Marginal likelihood,Statistics,Statistical hypothesis testing,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 1-2 | 1573-1375 |
Citations | PageRank | References |
1 | 0.37 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Buchner | 1 | 1 | 0.37 |