Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …

At the nanoscale, the investigation of structural features becomes fundamental as we can
establish relationships between cluster geometries and their physicochemical properties …

This report advances the hypothesis that multifunctional systems may be associated with
multifunnel potential and free energy landscapes, with particular focus on biomolecules. It …

We review the materials science applications of the nested sampling (NS) method, which
was originally conceived for calculating the evidence in Bayesian inference. We describe …

In this paper, we present a novel approach to accelerate the Bayesian inference process,
focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial …

We extend the nested sampling algorithm to simulate materials under periodic boundary
and constant pressure conditions, and show how it can be used to determine the complete …

Population annealing is a recent addition to the arsenal of the practitioner in computer
simulations in statistical physics and it proves to deal well with systems with complex free …

The theoretical analysis of many problems in physics, astronomy, and applied mathematics
requires an efficient numerical exploration of multimodal parameter spaces that exhibit …

The inverse problem of estimating the spatial distributions of elastic material properties from
noisy strain measurements is ill-posed. However, it is still typically treated as an optimisation …

We introduce a new class of sequential Monte Carlo methods called nested sampling via
sequential Monte Carlo (NS-SMC), which reformulates the essence of the nested sampling …