Uniformly sample from the unconstrained prior distribution

Generate new live points by performing rejection sampling across the entire prior distribution. This is highly inefficient as an LRPS, but may be useful for testing the behaviour of a nested sampling specification.

Usage

unif_cube()

Value

A list with class c("unif_cube", "ernest_lrps"). Can be used with ernest_sampler() to specify the sampling behaviour of a nested sampling run.

References

Speagle, J. S. (2020). Dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences. Monthly Notices of the Royal Astronomical Society, 493, 3132–3158. https://doi.org/10.1093/mnras/staa278

Examples

data(example_run)
lrps <- unif_cube()

ernest_sampler(example_run$log_lik_fn, example_run$prior, sampler = lrps)
#> Nested sampling specification <ernest_sampler>
#> No. Points: 500
#> 
#> ── Sampling Method 
#> • ! An abstract LRPS sampler <ernest_lrps>