Truncate a prior to a box

Stan's T[lower, upper], as an object. The returned prior is the original restricted to the box and renormalized by the mass it keeps, so its log_prob is a proper log density rather than the original shifted by an unknown constant. That matters here in a way it does not in Stan: the density is compared against a learned posterior in nle()'s MCMC target and summed with it in c2st() and the diagnostics, so a missing constant is a wrong answer rather than a constant offset.

Usage

prior_truncated(prior, lower = NULL, upper = NULL)

Arguments

prior

An nsbi_prior from a named family.

lower, upper

Truncation bounds. Numeric of length prior$dim, or length 1 to apply the same bound to every parameter. Give one or both; -Inf/Inf leaves that side alone.

Value

An nsbi_prior object.

Details

Renormalization needs the family's CDF, so prior has to come from a named family: prior_uniform(), prior_normal(), anything in prior_families, or a prior_independent() built from those. A prior_custom() is arbitrary R code with no CDF behind it; give it lower/upper there instead, which rejects out-of-support draws without claiming to renormalize.

See also

prior_families, prior_independent(), priors.

Examples

# A half-normal, the long way round (prior_half_normal() is the short one).
prior_truncated(prior_normal(mean = 0, sd = 1), lower = 0)
#> <nsbi_prior> type=truncated, dim=1
#>   lower: 0 
#>   theta[1] ~ normal(0, 1) T[0, ]

# A contact rate known to be between 0.1 and 2, log-normal in between.
prior <- prior_truncated(prior_lognormal(log(0.4), 0.5),
                         lower = 0.1, upper = 2)
range(sample_prior(prior, 100))
#> [1] 0.1397014 1.4725125