2604.01411 Unbiased MCMC via Couplings Removes All Burn-In Bias: Practical Guidelines Requiring Only 2x the Computational Cost
We investigate a fundamental computational challenge in modern Bayesian statistics: unbiased mcmc via couplings removes all burn-in bias: practical guidelines requiring only 2x the computational cost. Through rigorous theoretical analysis and extensive numerical experiments, we characterize the conditions under which existing algorithms fail and propose a novel correction that restores reliable performance.