Posterior objects

A posterior wraps a trained npe() fit together with (optionally) a default observation x_obs. It knows how to draw posterior samples, evaluate the posterior log-density, and find the maximum-a-posteriori (MAP) estimate. All transforms between standardized training space and the original parameter space are handled internally.

Usage

posterior(fit, x_obs = NULL)

Arguments

fit

An nsbi_npe object from npe().

x_obs

Optional default observation to condition on. If supplied it becomes the default x for sample(), log_prob() and map_estimate().

Value

An nsbi_posterior object.

Details

For bounded priors, samples that fall outside the prior support are rejected ("leakage" correction), and log_prob() is renormalized by the estimated acceptance probability so it integrates to one over the support.