Abstract
UltraNest is a general-purpose Bayesian inference package for parameter estimation and model comparison. It allows fitting arbitrary models specified as likelihood functions written in Python, C, C++, Fortran, Julia or R. With a focus on correctness and speed (in that order), UltraNest is especially useful for multi-modal or non-Gaussian parameter spaces, computational expensive models, in robust pipelines. Parallelisation to computing clusters and resuming incomplete runs is available.
Highlights
UltraNest is a general-purpose Bayesian inference package for parameter estimation and model comparison. It allows fitting arbitrary models specified as likelihood functions written in Python, C, C++, Fortran, Julia or R
With a focus on correctness and speed, UltraNest is especially useful for multi-modal or non-Gaussian parameter spaces, computational expensive models, in robust pipelines
The approaches used in UltraNest are highlighted there as well
Summary
UltraNest is a general-purpose Bayesian inference package for parameter estimation and model comparison. It allows fitting arbitrary models specified as likelihood functions written in Python, C, C++, Fortran, Julia or R. With a focus on correctness and speed (in that order), UltraNest is especially useful for multi-modal or non-Gaussian parameter spaces, computational expensive models, in robust pipelines. While several open source Bayesian model fitting packages are available that can be tied to existing models, they are difficult to run such that the result is reliable and user interaction is minimized. Current and upcoming large astronomical surveys require characterising a large number of highly diverse objects, which requires reliable analysis pipelines This is what UltraNest was developed for. For potentially complex posteriors where the user is willing to invest computation for obtaining a gold-standard exploration of the entire posterior distribution in one run, UltraNest was developed
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