GetDist: a Python package for analysing Monte Carlo samples

By A. Lewis. 2019

877 citations recorded by the source corpus.

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. [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.

View Paper: https://www.semanticscholar.org/paper/3d278138e9ab525232932853e56a96c98dc5c51d

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Citations in the corpus

• Early dark energy does not restore cosmological concordance (2020, methodology): “We analyze the MCMC chains using GetDist (Lewis (2019)).6 6.1.”
• CMB lensing from Planck PR4 maps (2022, methodology): “We use the Markov Chain Monte Carlo sampling method with Cobaya7 [38] to estimate cosmological parameters from the calculated likelihoods, and analyse the resulting chains using GetDist8 [39].”
• Combining full-shape and BAO analyses of galaxy power spectra: a 1.6% CMB-independent constraint on H0 (2020, methodology): “The posterior contours for α obtained from these analyses (as well as with the fiducial set of hyperparameters) are shown in figure 2, created with the getdist code [101],15 which is part of the CosmoMC package [102, 103].”
• Planck2018 results (2018, methodology): “chains using the GetDist package (Lewis 2019), following the same conventions as Planck Collaboration XIII (2016).”
• Constraining early dark energy with large-scale structure (2020, result): “[76] A. Lewis, GetDist: a Python package for analysing Monte Carlo samples, arXiv e-prints (2019) arXiv:1910.13970 [1910.13970].”
• The Hubble tension in light of the Full-Shape analysis of Large-Scale Structure data (2020, methodology): “11 The plots have been obtained using the GetDist package [86].”
• KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints (2020, methodology): “The figures in this work were created with matplotlib (Hunter 2007) and getdist (Lewis 2019), making use of the numpy (Oliphant 2006) and scipy (Jones et al. 2001) software packages.”
• Improved reconstruction of a stochastic gravitational wave background with LISA (2020, methodology): “The results were analyzed and the contour plots were produced using GetDist [60].”