nested sampling

Since nested sampling was proposed in 2004, it has been used in multiple settings within the field of astronomy.

A note on nested sampling.

Rao (1973) studied double nested sampling in the context of sampling and estimation for a single variable of interest.

One paper suggested using nested sampling for cosmological model selection and object detection, as it "uniquely combines accuracy, general applicability and computational feasibility."

The nested sampling algorithm is a computational approach to the problem of comparing models in Bayesian statistics, developed in 2004 by physicist John Skilling.

A refinement of the nested sampling algorithm to handle multimodal posteriors has also been suggested as a means of detecting astronomical objects in existing datasets.

The nested sampling algorithm was developed by John Skilling specifically to approximate these marginalization integrals, and it has the added benefit of generating samples from the posterior distribution .

A highly modular Python parallel implementation of Nested Sampling for statistical physics and condensed matter physics applications is publicly available from GitHub [3].

Other applications of nested sampling is in the field of finite element updating where nested sampling is used to choose an optimal finite element model and this was applied to structural dynamics.