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Run nested sampling to estimate Bayesian evidence

Source: R/generate.R
generate.ernest_sampler.Rd

Executes the nested sampling algorithm, iteratively replacing the worst live point with a new sample from a likelihood-restricted prior until a stopping criterion is met.

Usage

# S3 method for class 'ernest_sampler'
generate(
  x,
  ...,
  max_iterations = NULL,
  max_calls = NULL,
  min_logz = 0.05,
  show_progress = NULL
)

Arguments

x

An object of class ernest_sampler or ernest_run.

...

Arguments passed on to compile.ernest_run

seed

[Deprecated] seed is no longer supported; set RNG with the seed argument of ernest_sampler().

clear

Logical. If TRUE, clears results from previous runs before compiling. If FALSE, retains previous results and validates live points.

max_iterations

Optional positive integer. The maximum number of iterations to perform. If NULL, this criterion is ignored.

max_calls

Optional positive integer. The maximum number of calls to the likelihood function. If Inf, this criterion is ignored.

min_logz

Non-negative double. The minimum log-ratio between the current estimated evidence and the remaining evidence. If zero, this criterion is ignored.

show_progress

Logical. If TRUE, displays a progress spinner and iteration counter during sampling. If NULL, then the parameter is inferred based on the value of the rlib_message_verbosity option.

Value

An object of class ernest_run, inheriting from ernest_sampler, with additional components:

  • n_iter: Integer. Number of iterations.

  • n_calls: Integer. Total number of likelihood function calls.

  • log_lik: double(n_iter + n_points). Log-likelihoods for each sample.

  • log_volume: double(n_iter + n_points). Estimated log-prior volumes at each iteration.

  • log_weight: double(n_iter + n_points). Unnormalised log-weights for each sample.

  • log_evidence: double(n_iter + n_points). Cumulative log-evidence estimates at each iteration.

  • log_evidence_var: double(n_iter + n_points). Variance of the log-evidence estimate at each iteration.

  • information: double(n_iter + n_points). KL divergence between the prior and posterior, estimated at each iteration.

  • samples: matrix(nrow = n_iter + n_points, ncol = n_dim). Parameter values for each sample.

  • samples_unit: matrix(nrow = n_iter + n_points, ncol = n_dim). Parameter values for each sample in unit hypercube representation.

  • id: integer(n_iter + n_points). Unique integer identifiers for each sample from the live set (ranging from 1 to n_points).

  • points: integer(n_iter + n_points). Number of live points present at each iteration.

  • calls: integer(n_iter + n_points). Number of calls used to generate a new live point at each iteration.

  • birth: integer(n_iter + n_points). Iteration at which each sample was first created (ranging from 0 to n_iter).

Details

At least one of max_iterations, max_calls, or min_logz must specify a valid stopping criterion. Setting min_logz to zero while leaving max_iterations and max_calls at their defaults will result in an error.

If x is an ernest_run object, the stopping criteria are checked against the current state of the run. An error is thrown if the stopping criteria have already been satisfied by x.

The min_logz parameter controls the relative tolerance for the remaining evidence in the unexplored parameter space. Sampling stops when the estimated remaining evidence is sufficiently small compared to the accumulated evidence.

References

Skilling, J. (2006). Nested Sampling for General Bayesian Computation. Bayesian Analysis, 1(4), 833–859. doi:10.1214/06-BA127

Examples

prior <- create_uniform_prior(lower = c(-1, -1), upper = 1)
#> New names:
#> • `Uniform` -> `Uniform...1`
#> • `Uniform` -> `Uniform...2`
ll_fn <- function(x) -sum(x^2)
sampler <- ernest_sampler(ll_fn, prior, n_point = 100)
sampler
#> nested sampling specification <ernest_sampler>
#> • No. Points: 100
#> • LRPS Method: rwmh_cube
#> 
#> ernest LRPS method <rwmh_cube/ernest_lrps>
#> • Dimensions: 2
#> • No. Log-Lik Calls: 0
#> • No. Accepted Proposals: 0
#> • No. Steps: 25
#> • Target Acceptance: 0.5
#> • Step Size: 1.000

# Stop sampling after a set number of iterations or likelihood calls.
generate(sampler, max_iterations = 100)
#> ℹ Created 100 live points.
#> ✔ `max_iterations` reached (100).
#> nested sampling results <ernest_run/ernest_sampler>
#> • No. Points: 100
#> • LRPS Method: rwmh_cube
#> ────────────────────────────────────────────────────────────────────────────────
#> • No. Iterations: 100
#> • No. Calls: 176
#> • Log. Evidence: -0.6444 (± 0.1312)

# The final number of calls may exceed `max_calls`, as `generate`
# only checks the number of calls when removing a live point.
generate(sampler, max_calls = 2600)
#> ℹ Created 100 live points.
#> ✔ `max_calls` surpassed (2603 > 2600).
#> nested sampling results <ernest_run/ernest_sampler>
#> • No. Points: 100
#> • LRPS Method: rwmh_cube
#> ────────────────────────────────────────────────────────────────────────────────
#> • No. Iterations: 227
#> • No. Calls: 2603
#> • Log. Evidence: -0.6349 (± 0.07714)

# Use the default stopping criteria
if (FALSE)  generate(sampler)  # \dontrun{}