A prior in neuralsbi is a lightweight object (class nsbi_prior) that knows
how to (a) draw samples and (b) evaluate its log-density. Bounded priors also
carry lower/upper support limits, which are used to reject out-of-support
posterior samples ("leakage" correction).
Details
There are four ways to build one. prior_uniform() is the box prior most
benchmark tasks use. prior_normal() and the named families in
prior_families (log-normal, exponential, gamma, beta, Student-t, Cauchy
and the half versions of the last two) give one marginal per parameter,
under Stan's argument names. prior_independent() multiplies those together
into a joint prior, and prior_truncated() bounds one, renormalizing the
density by the mass it keeps. prior_custom() takes a sampler and a density
you write yourself, for anything the families do not cover.
Prefer a named family over prior_custom() where one fits. A family carries
its support bounds into the leakage correction, survives prior_truncated()
with an exact normalizing constant, and is what stan_code() writes out as
a sampling statement; a custom prior does none of those.