DNest4: Diffusive Nested Sampling in C++ and Python

Understand

In probabilistic (Bayesian) inferences, we typically want to compute properties of the posterior distribution, describing knowledge of unknown quantities in the context of a particular dataset and the assumed prior information.

• The marginal likelihood, also known as the "evidence", is a key quantity in Bayesian model selection.
• The Diffusive Nested Sampling algorithm, a variant of Nested Sampling, is a powerful tool for generating posterior samples and estimating marginal likelihoods.
• It is effective at solving complex problems including many where the posterior distribution is multimodal or has strong dependencies between variables.