Posterior log-density

Usage

# S3 method for class 'nsbi_mcmc_posterior'
log_prob(post, theta, x = NULL, normalize = TRUE, ...)

log_prob(post, theta, x = NULL, ...)

# S3 method for class 'nsbi_posterior'
log_prob(
  post,
  theta,
  x = NULL,
  normalize = TRUE,
  n_normalization = 10000L,
  ...
)

Arguments

post: An nsbi_posterior object.
theta: Matrix (or vector) of parameter values to evaluate.
x: Observation to condition on (defaults to x_obs).
normalize: For bounded priors, renormalize by the estimated acceptance probability and return -Inf outside the prior support.
...: Passed to methods.
n_normalization: Number of draws used to estimate the normalizing (acceptance) constant when normalize = TRUE. If none of them land inside the prior support, the estimate is floored at 1 / n_normalization to avoid log(0) and a warning says so – the same warning sample() raises when rejection sampling comes up empty. These draws are internal to the estimate (always taken from a fixed, throwaway stream) and never advance the caller's own RNG, unlike sample(), which is documented to consume randomness.

Value

Numeric vector of log posterior densities. For a posterior built from an nle() or nre() fit the value is unnormalized – the evidence \(p(x)\) is not available – so differences between two theta are meaningful but the absolute level is not.