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Create a new likelihood-restricted prior sampler (LRPS)

Source: R/lrps-class.R
new_ernest_lrps.Rd

Constructs an LRPS, accepted by ernest_sampler() and used to create new points during a nested sampling run.

Usage

new_ernest_lrps(
  unit_log_fn = NULL,
  n_dim = NULL,
  max_loop = getOption("ernest.max_loop", 1000000L),
  cache = NULL,
  ...,
  .class = NULL,
  .call = caller_env()
)

Arguments

unit_log_fn

[function]
Takes a matrix of points in the unit cube and returns a numeric vector of log-likelihood values. Optional, and updated when called by ernest_sampler().

n_dim

[integer(1)]
Number of dimensions within the prior space. Optional, and updated when called by ernest_sampler().

max_loop

[integer(1)]
Maximum number of attempts to generate points. Inferred from the ernest.max_loop option.

cache

[environment] Environment for caching information required for the specific LRPS method. Created if left NULL.

...

<dynamic-dots> Name-value pairs for additional elements for ernest_lrps' subclasses

.class

[character()]
Names for the subclass of ernest_lrps.

.call

The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.

Value

[ernest_lrps], a named list containing the arguments provided.

Details

Nested sampling relies on generating a series of points in the prior space with increasing log-likelihood values. This is accomplished using a likelihood-restricted prior sampler (LRPS), which generates independent and identically distributed points from the prior, subject to a hard likelihood constraint.

To create your own LRPS, subclass new_ernest_lrps and provide S3 methods for propose() and update_lrps() for your subclass.