Nested sampling in practice

Abstract

For problems requiring both model comparison and parameter estimation, nested sampling is an attractive choice because it provides an estimate of the normalized posterior. The critical assumption of nested sampling is that exploration proceeds regularly toward the region of maximum likelihood. However, in practice, achieving such regular compression is far from trivial, particularly for realistic, multi-modal problems. This paper offers a comparison of settings required for nested sampling with the purpose of maximizing consistency and robustness. In particular, the ensemble size, amount of exploration, and likelihood function are compared, and approximate settings recommended, for two example problems in acoustic data analysis.