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LoLab-VU/Gleipnir: Bayesian parameter inference and model selection for biological models using Nested Sampling in Python

Blake A. Wilson; Carlos F. Lopez

Researcher(s)
Blake A. Wilson
Supervisor(s)
Carlos F. Lopez

Gleipnir is a python toolkit that provides an easy to use interface for Bayesian parameter inference and model selection using Nested Sampling. It has a built-in implementation of the Nested Sampling algorithm but also provides a common interface to the Nested Sampling implementations MultiNest, PolyChord, dyPolyChord, DNest4, and Nestle. Although Gleipnir provides a general framework for running Nested Sampling simulations, it was created with biological models in mind. It therefore supplies additional tools for working with biological models in the PySB format (see the PySB Utilities section). Likewise, Gleipnir's API was designed to be familiar to users of PyDREAM and simplePSO, which are primarily used for biological model calibration.

v0.26.2 includes a bug fix in the posterior_moments function for the nsbase class.
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