158 citations · 158 across the 5 of their papers we have counts for
5 papers
A space of inference spaces in the space sciences - parametric Bayesian inference in astronomy, cosmology and particle physics
Johannes Buchner
A sample of parametric Bayesian inference applications from astronomy, cosmology and particle physics is studied, augmented by mock data sets and toy problems. The parameter spaces…
First analytical coverage bounds of a fully specified nested sampling algorithm
Johannes Buchner
Nested sampling is a Monte Carlo algorithm for posterior estimation and Bayesian model comparison. It maintains a population of live points sampled from the prior, and at each…
Mode Collapse in Nested Sampling
Johannes Buchner
Nested Sampling is a Monte Carlo algorithm enabling posterior estimation and Bayesian model comparison, and is especially robust in multi-modal posteriors. This is because nested s…
Snowballing Nested Sampling
Johannes Buchner
A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subseq…
Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner +20
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…