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Generates a stratified matrix of flow values by pairing each z-ordinate with a different parameter set from the posterior distribution. This collapses the nested Monte Carlo structure into a single pass, simultaneously sampling natural variability (via stratified z-ordinates) and knowledge uncertainty (via varying parameters). Used internally by rfa_simulate() for expected-only mode.

Usage

flow_frequency_sampler_expected(
  bestfit_params,
  freq_dist = "LP3",
  strat_dist = "ev1",
  Nbin = NULL,
  Mevent = NULL
)

Arguments

bestfit_params

Data frame of distribution parameters from RMC-BestFit. Must have Nbin * Mevent rows (one parameter set per z-ordinate). For LP3: columns are mean (log), sd (log), skew (log). For GEV: columns are location, scale, shape.

freq_dist

Character. Distribution type. Either "LP3" (default) or "GEV".

strat_dist

Character. Probability space for stratification bins. Passed to stratified_sampler(). One of "ev1" (default), "normal", or "uniform". See stratified_sampler() for details.

Nbin

Integer. Number of stratified bins. Default is 50.

Mevent

Integer. Number of events per bin. Default is 200.

Value

A list containing:

flow

Matrix of sampled flow values [Mevent x Nbin]

nbins

Number of stratified bins

mevents

Number of events per bin

weights

Probability weights for each bin from stratified_sampler()

Examples

# Using a pre-loaded parameter set (all 10,000 parameter sets)
result <- flow_frequency_sampler_expected(jmd_bf_parameter_sets,
                                          Nbin = 20, Mevent = 500)
dim(result$flow)  # 500 x 20
#> [1] 500  20

# Using bootstrapped parameter samples
jmd_samples <- bootstrap_vfc(
  c(jmd_vfc_parameters$mean_log,
    jmd_vfc_parameters$sd_log,
    jmd_vfc_parameters$skew_log),
  dist = "LP3",
  ERL  = jmd_vfc_parameters$erl)

jmd_result <- flow_frequency_sampler_expected(
  jmd_samples$params,
  freq_dist = "LP3",
  Nbin      = 20,
  Mevent    = 500)