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

Markov chain Monte Carlo–based Bayesian data analysis has now become the method of
choice for analyzing and interpreting data in almost all disciplines of science. In astronomy …

The curvature parameter tension between Planck 2018, cosmic microwave background
(CMB) lensing, and baryon acoustic oscillation (BAO) data is measured using the …

The growing field of large-scale time domain astronomy requires methods for probabilistic
data analysis that are computationally tractable, even with large data sets. Gaussian …

Context. Proxima Centauri is the closest star to the Sun. This small, low-mass, mid M dwarf is
known to host an Earth-mass exoplanet with an orbital period of 11.2 days within the …

Abstract Markov Chain Monte Carlo (MCMC) methods for sampling probability density
functions (combined with abundant computational resources) have transformed the …

We introduce a stable, well tested Python implementation of the affine-invariant ensemble
sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The …

We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm
in which the number of “live points” varies to allocate samples more efficiently. In empirical …

Circumbinary planets, those that orbit around both stars of a central binary star system,
challenge our understanding of planet formation. With only 12 binary systems known to host …

We review state-of-the-art Monte Carlo (MC) techniques for computing fluid coexistence
properties (Gibbs simulations) and adsorption simulations in nanoporous materials such as …