First analytical coverage bounds of a fully specified nested sampling algorithm

Abstract: Nested sampling is a Monte Carlo algorithm for posterior estimation and Bayesian model comparison. It maintains a population of $K$ live points sampled from the prior, and at each iteration discards the lowest-likelihood point and replaces it with a new sample drawn from the prior restricted to exceed the discarded likelihood. Achieving this likelihood-restricted prior sampling efficiently and reliably is the central computational challenge. For low-to-moderate dimensional problems, MLFriends is a general and robust region-based approach that constructs a proposal region by bootstrap aggregation over the current live points and rejects proposals outside this region. We present a self-contained mathematical formulation of MLFriends and derive, under a homogeneous Binomial point process model for the live points, heuristic bounds on the expected fraction of the likelihood-restricted prior not covered by the proposal region. These bounds decay as $(\frac{1}{3}Km)^{-3/2}$, where $m$ is the number of bootstrap rounds, and are negligibly small for practical parameter choices. We show heuristically that the resulting bias in the marginal likelihood estimate is negligible compared to the inherent statistical variance of a nested sampling run. While a fully rigorous treatment remains an open problem, these results provide the first analytical characterisation of a fully specified and practically implementable nested sampling algorithm, without assuming an idealised or asymptotic sampling procedure.