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Comparison of Step Samplers for Nested Sampling
Johannes Buchner
Bayesian inference with nested sampling requires a likelihood-restricted prior sampling method, which draws samples from the prior distribution that exceed a likelihood threshold.…
Nested Sampling Methods
Johannes Buchner
Nested sampling (NS) computes parameter posterior distributions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of compl…
UltraNest -- a robust, general purpose Bayesian inference engine
Johannes Buchner
UltraNest is a general-purpose Bayesian inference package for parameter estimation and model comparison. It allows fitting arbitrary models specified as likelihood functions writte…
Collaborative Nested Sampling: Big Data vs. complex physical models
Johannes Buchner
The data torrent unleashed by current and upcoming astronomical surveys demands scalable analysis methods. Many machine learning approaches scale well, but separating the instrumen…
A statistical test for Nested Sampling algorithms
Johannes Buchner
Nested sampling is an iterative integration procedure that shrinks the prior volume towards higher likelihoods by removing a "live" point at a time. A replacement point is drawn un…