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GetDist: a Python package for analysing Monte Carlo samples

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GetDist analyzes correlated Monte Carlo samples, suggesting improved Bayesian inference techniques.

GetDist efficiently analyzes correlated Monte Carlo samples, enhancing data interpretation and visualization.
The package utilizes kernel density estimation to compute marginalized densities for one- and two-dimensional data.
GetDist addresses issues from MCMC with a linear boundary kernel and multiplicative bias correction for accuracy.
The user-friendly package generates publication-ready figures and provides diagnostic tools for convergence assessment.

Antony Lewis (2025) studied this question.

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