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dynesty — dynesty 2. 1. 5 documentation dynesty is a Pure Python, MIT-licensed Dynamic Nested Sampling package for estimating Bayesian posteriors and evidences See Crash Course and Getting Started for more information
Getting Started — dynesty 2. 1. 5 documentation dynesty tries to avoid constructing bounding distributions early in the run to avoid issues where the bounds can significantly exceed the unit cube For instance, in most cases the bounding distribution of the initial set of points by construction will exceed the bounds of the unit cube when enlarge > 1
dynesty·PyPI Several Jupyter notebooks that demonstrate most of the available features of the code can be found here If you find the package useful in your research, please cite at least both of these references: and ideally also papers describing the underlying methods (see the documentation for more details)
Examples — dynesty 2. 1. 5 documentation - Read the Docs This page highlights several examples on how dynesty can be used in practice, illustrating both simple and more advanced aspects of the code Jupyter notebooks containing more details are available on Github
Background — dynesty 2. 1. 5 documentation - Read the Docs Nested sampling is a method for estimating the Bayesian evidence Z first proposed and developed by John Skilling The basic idea is to approximate the evidence by integrating the prior in nested “shells” of constant likelihood