GetDist: a Python package for analysing Monte Carlo samples

doi:doi:10.1088/1475-7516/2025/08/025

GetDist: a Python package for analysing Monte Carlo samples

2025-08-08

0

PRE

AI

A

Antony Lewis

DOI：doi:10.1088/1475-7516/2025/08/025

Original

Original request for help

Save

Abstract

En 中文

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 PythonGetDist 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. [1], 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.

Keywords:

Monte Carlo methods

Bayesian inference

Kernel Density Estimation

Markov Chain Monte Carlo

parameter space analysis

GetDist: a Python package for analysing Monte Carlo samples

Abstract

Journal

Organization