MCMC

The problem of sampling exchangeable random variables arises in many Bayesian inference tasks, especially in data imputation given a privatized summary statistics. These permutation-invariant joint distributions often have dependency structures that …

Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-Gibbs algorithm standing out as a well-known example. Despite their …