# GetDist: a Python package for analysing Monte Carlo samples

@article{Lewis2019GetDistAP, title={GetDist: a Python package for analysing Monte Carlo samples}, author={A. Lewis}, journal={arXiv: Instrumentation and Methods for Astrophysics}, year={2019} }

Monte Carlo techniques, including MCMC and other methods, are widely used and generate sets of samples from a parameter space of interest that can be used to infer or plot quantities of interest. This note outlines methods used the Python GetDist package to calculate 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… Expand

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