Nested sampling methods

Nested sampling (NS) computes parameter posterior distributions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of complex, potentially multi-modal posteriors until a well-defined termination point. A systematic literature review nested algorithms variants is presented. We focus on complete algorithms, including solutions to likelihood-restricted prior sampling, parallelisation, diagnostics. The relation between number live points, dimensionality computational cost studied for two algorithms. new formulation NS presented, which casts space exploration as search tree data structure. Previously published ways obtaining robust error estimates dynamic variations points presented special cases this formulation. online diagnostic test based previous insertion rank order work. survey methods concludes with outlooks future research.