Summarize a nested sampling run

Returns a concise summary of an ernest_run object, including key statistics and a description of the posterior distribution.

Usage

# S3 method for class 'ernest_run'
summary(object, ...)

Arguments

object

[ernest_run]
Results from a nested sampling run.

...

These dots are for future extensions and must be empty.

Value

[summary.ernest_run] A named list, containing:

• nlive: [integer(1)] Number of points in the live set.

• niter: [integer(1)] Number of iterations.

• neval: [integer(1)] Number of likelihood evaluations.

• log_evidence: [numeric(1)] Log-evidence estimate.

• log_evidence_err: [numeric(1)] Standard error of log-evidence.

• information: [numeric(1)] Estimated Kullback-Leibler divergence between the prior and posterior.

• reweighted_samples: [posterior::draws_matrix] Posterior samples, resampled by normalized weights.

• mle: [list] Maximum likelihood estimate extracted during the run, stored in a list with the elements:

  • log_lik: [double(1)] The maximum log-likelihood value.

  • original, unit_cube: [double(n_dim)] The parameter values at the MLE, expressed in the original parameter space and within the unit cube.

• posterior: [tibble] with columns for the posterior mean, sd, median, and the 15th and 85th percentiles for each parameter.

• seed: The RNG seed used.

See also

• generate() for details on the ernest_run object.

• as_draws() for details on how posterior samples are extracted.

Examples

data(example_run)
run_sm <- summary(example_run)
run_sm
#> Summary of nested sampling run:
#> ── Run Information ─────────────────────────────────────────────────────────────
#> * No. points: 1000
#> * Iterations: 9456
#> * Likelihood evals.: 207001
#> * Log-evidence: -9.1176 (± 0.0833)
#> * Information: 4.930
#> * RNG seed: 42
#> ── Posterior Summary ───────────────────────────────────────────────────────────
#> # A tibble: 3 × 6
#>   variable     mean    sd  median   q15   q85
#>   <chr>       <dbl> <dbl>   <dbl> <dbl> <dbl>
#> 1 x        -0.00572  2.79 -0.0174 -1.94  2.00
#> 2 y         0.0302   2.80  0.0165 -1.91  2.04
#> 3 z        -0.00378  2.82  0.0192 -1.96  1.97
#> ── Maximum Likelihood Estimate (MLE) ───────────────────────────────────────────
#> * Log-likelihood: -2.6803
#> * Original parameters: 0.0097, -0.0096, and -0.0231
run_sm$posterior
#> # A tibble: 3 × 6
#>   variable     mean    sd  median   q15   q85
#>   <chr>       <dbl> <dbl>   <dbl> <dbl> <dbl>
#> 1 x        -0.00572  2.79 -0.0174 -1.94  2.00
#> 2 y         0.0302   2.80  0.0165 -1.91  2.04
#> 3 z        -0.00378  2.82  0.0192 -1.96  1.97