Load a precomputed example nested sampling run generated using the ernest
package. It demonstrates a typical output from a nested sampling run on a
simple 3-dimensional Gaussian likelihood, with a uniform prior over each
dimension. This dataset is intended for use in documentation, tutorials,
and gainining experience with ernest_run's S3 methods.
Source
This example problem comes from the crash course for the dynesty Python-based nested sampling software.
Details
The likelihood used to generate the points is \(MVN(0, \Sigma)\), with
each variance in \(\Sigma\) set to 1 and each covariance set to 0.95.
The prior for each parameter is uniform on the interval [-10, 10\].
This run uses the following non-default settings:
log_lik: A 3D multivariate Gaussian with mean zero and covariance matrixdiag(0.95, 3).prior: Uniform over each dimension (x, y, z) in the range [-10, 10]. Seed: 42
View the $spec element of example_run to see the full R specification
of the likelihood and prior.
[-10, 10]: R:-10,%2010%5C [-10, 10]: R:-10,%2010