Simulation-Based Calibration (SBC)

Repeatedly draws a "true" parameter from the prior, simulates data, and ranks the true parameter within posterior samples conditioned on that data. If the posterior is well calibrated, the ranks are uniformly distributed.

Usage

sbc(
  fit,
  simulator,
  prior = fit$prior,
  n_sbc = 200L,
  n_posterior_samples = 1000L,
  seed = NULL,
  chunk_size = NULL
)

Arguments

fit

An nsbi_npe fit (amortized posterior).

simulator

The simulator used for inference.

prior

The prior used for inference (defaults to fit$prior).

n_sbc

Number of SBC trials (fresh (theta, x) pairs).

n_posterior_samples

Posterior draws per trial (rank resolution).

seed

Optional seed.

chunk_size

Rows per simulator call; see nsbi_parallel. The n_sbc simulations run across future workers when a plan is set; the ranking loop that follows calls the trained network and always runs locally.

Value

An object of class nsbi_sbc with the rank matrix and a per-parameter uniformity test.