The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
GetDist: a Python package for analysing Monte Carlo samples
Open accessAbstract
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.
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Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Planck 2018 results: VI. Cosmological parameters
Planck Collaboration, Aghanim, N., Akrami, Y., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., et al. (2020) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201833910 · Cited by 14,258
Self-citationPlanck 2013 results. XVI. Cosmological parameters
Planck Collaboration, Ade, P. A. R., Aghanim, N., Armitage-Caplan, C., Arnaud, M., Ashdown, M., Atrio-Barandela, F., Aumont, J., Baccigalupi, C., Banday, A. J., et al. (2014) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201321591 · Cited by 6,521
Self-citationCosmological parameters from CMB and other data: A Monte Carlo approach
Lewis, A., & Bridle, S. (2002) · Physical Review D · doi:10.1103/physrevd.66.103511 · Cited by 3,495 · cited 1×
“Example GetDist ‘triangle’ plot of MCMC parameter samples, here taken from the Planck 2018 baseline cosmological parameter chains [9] (generated using the fast-parameter dragging sampler [13–15]).”
Self-citationKernel density estimation via diffusion
Botev, Z. I., Grotowski, J. F., & Kroese, D. P. (2010) · The Annals of Statistics · doi:10.1214/10-aos799 · Cited by 1,689
Feroz, F., & Hobson, M. P. (2008) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2007.12353.x · Cited by 1,658
Skilling, J. (2004) · AIP Conference Proceedings · doi:10.1063/1.1835238 · Cited by 925
Cobaya: code for Bayesian analysis of hierarchical physical models
Torrado, J., & Lewis, A. (2021) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2021/05/057 · Cited by 692
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Cited by
DESI DR2 results. II. Measurements of baryon acoustic oscillations and cosmological constraints
Abdul Karim, M., Aguilar, J., Ahlen, S., Alam, S., Allen, L., Prieto, C. A., Alves, O., Anand, A., Andrade, U., Armengaud, E., et al. (2025) · Physical Review D · doi:10.1103/tr6y-kpc6 · Cited by 856
Revisiting the evidence for dynamical dark energy: from DES-SN5Y to DES-Dovekie
Kandel, A., Atamurotov, F., Güdekli, E., Channuie, P., & Rakhimova, G. (2026) · The European Physical Journal C · doi:10.1140/epjc/s10052-026-16010-6 · Cited by 1
Metadata
- Authors
- Lewis, A.
- Venue
- Journal of Cosmology and Astroparticle Physics
- Publisher
- IOP Publishing
- Published
- 2025-08-01
- Volume
- 2025
- Issue
- 08
- Pages
- 025
- ISSN
- 1475-7508 (ISSN-L)
- 1475-7516
- OpenAlex
- W4413247502
- Cited by
- 280
- References
- 22
- Type
- software paper
- Language
- en
- Open access
- hybrid
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