joshspeagle/dynesty

Dynamic Nested Sampling package for computing Bayesian posteriors and evidences

What it solves

dynesty is designed to compute Bayesian posteriors and evidences, which are essential for model selection and parameter estimation in scientific research.

How it works

It implements a Dynamic Nested Sampling algorithm. This approach allows the process of sampling from the probability distribution of a model's parameters to be more efficient and more accurate than traditional methods.

Who it’s for

Researchers and scientists who need to perform Bayesian inference and compute evidence for their models.

Highlights

  • Pure Python: The package is written entirely in Python, making it easier to install and install across different environments.
  • Dynamic Nested Sampling: Uses a specialized sampling technique to optimize the evidence computation.
  • Comprehensive Documentation: Provides detailed guides and the restdocs documentation.
  • Demos: Includes Jupyter notebooks that demonstrate the core features of the code.

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