bayes_mh_censored: Bayesian Inference via Metropolis-Hastings MCMC Algorithm

In CompRiskRel: Reliability and Competing Risks Analysis under Hybrid Censoring

Bayesian Inference via Metropolis-Hastings MCMC Algorithm

Description

Bayesian Inference via Metropolis-Hastings MCMC Algorithm

Usage

bayes_mh_censored(
  log_posterior,
  init_par,
  n_sim = 5000,
  burn_in = 1000,
  proposal_sd = 0.1
)

Arguments

log_posterior	Function evaluating log-posterior density.
init_par	Initial parameter values.
n_sim	Total MCMC sample size.
burn_in	Discarded burn-in samples.
proposal_sd	Proposal standard deviation vector.

Value

S3 object of class bayes_fit.

Examples

log_post <- function(th) {
  if (th[1] <= 0) return(-Inf)
  dexp(th[1], rate = 1, log = TRUE) +
    sum(dexp(c(0.5, 1.2, 0.8), rate = th[1], log = TRUE))
}
bayes_mh_censored(
  log_post, init_par = c(1.0),
  n_sim = 1000, burn_in = 200
)

CompRiskRel documentation built on Aug. 5, 2026, 9:08 a.m.