Use rejection sampling across the entire prior distribution to create new live points. This is highly inefficient as an LRPS, but may be useful for testing the behaviour of a nested sampling specification.
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. doi:10.1093/mnras/staa278
See also
Other ernest_lrps:
mini_balls(),
multi_ellipsoid(),
no_underrun(),
rwmh_cube(),
slice_rectangle(),
unif_ellipsoid()
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
#> • LRPS Method: unif_cube
#>
#> ernest LRPS method <unif_cube/ernest_lrps>
#> • Dimensions: 3
#> • No. Log-Lik Calls: 0