Summary of importance-sampling diagnostics

Convenience accessor returning the headline IS-quality numbers for a regime-(iii) fit: effective sample size and its ratio to is_size, the largest self-normalised weight, the support fraction (proportion of draws that received a finite weight), and the Monte-Carlo standard error of the final KLD estimate. Returns NA fields for regimes that do not use importance sampling.

Usage

ess_summary(fit)

Arguments

fit

A gmm_fit.

Value

A list of numeric scalars (or NAs where not applicable), plus a logical support_truncated flag that is TRUE when the fit's auto-selected proposal replaced an unbounded target-support end with a finite, data-derived working box (so support_fraction reflects only that finite region and the target tail beyond it was not sampled). The finite box itself is recorded in fit@diagnostics$working_bounds.

Details

Validation-side numbers (validation_*) are populated only when the fit was called with validation_size > 0.

See also

Other diagnostics: bic_aic(), ess_trace(), gmm_anneal_path(), gmm_conditional_entropy(), gmm_entropy(), gmm_evidence(), gmm_fit_ensemble(), gmm_fit_quality(), gmm_independence_graph(), gmm_mutual_information(), hellinger_mc(), kld_trace(), proxy_functional_ci()

Examples

fit <- fit_proxymix(banana_target(), N = 3L, regime = "kld",
                    is_size = 1500L, max_iter = 20L, seed = 1L,
                    validation_size = 1500L)
ess_summary(fit)
#> $is_size
#> [1] 1500
#> 
#> $ess
#> [1] 1048.118
#> 
#> $ess_relative
#> [1] 0.6987454
#> 
#> $max_weight
#> [1] 0.005472202
#> 
#> $support_fraction
#> [1] 1
#> 
#> $support_truncated
#> [1] FALSE
#> 
#> $mc_se_kld
#> [1] 0.009224772
#> 
#> $validation_size
#> [1] 1500
#> 
#> $validation_ess
#> [1] 1111.561
#> 
#> $validation_ess_relative
#> [1] 0.7410408
#> 
#> $validation_max_weight
#> [1] 0.003698124
#> 
#> $validation_support_fraction
#> [1] 1
#> 
#> $validation_kld
#> [1] 0.02868125
#>