2019/10/30 by Antony Lewis, Lewis, Antony · 112 citations
Decision Sciences · Mathematics · #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Data Analysis #FOS: Physical sciences #Forecasting Techniques and Applications #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1910.13970
openalex publication_date 2019/10/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Monte Carlo techniques, including MCMC and other methods, are widely used in Bayesian inference to generate sets of samples from a parameter space of interest. The Python GetDist package provides tools for analysing these samples and calculating marginalized one- and two-dimensional densities using Kernel Density Estimation (KDE). Many Monte Carlo methods produce correlated and/or weighted samples, for example produced by MCMC, nested, or importance sampling, and there can be hard boundary priors. GetDist's baseline method consists of applying a linear boundary kernel, and then using multiplicative bias correction. The smoothing bandwidth is selected automatically following Botev et al., based on a mixture of heuristics and optimization results using the expected scaling with an effective number of samples (defined here to account for both MCMC correlations and weights). Two-dimensional KDE uses an automatically-determined elliptical Gaussian kernel for correlated distributions. The package includes tools for producing a variety of publication-quality figures using a simple named-parameter interface, as well as a graphical user interface that can be used for interactive exploration. It can also calculate convergence diagnostics, produce tables of limits, and output in LaTeX, and is publicly available.
unimpeded: A Public Nested Sampling Database for Bayesian Cosmology