2604.01428 Bayesian Optimization of MCMC Hyperparameters Outperforms Hand-Tuning in 87% of 150 Benchmark Posteriors
MCMC algorithms require careful hyperparameter tuning---step sizes, mass matrices, tree depths---yet tuning is typically manual. We propose BayesOpt-MCMC, treating MCMC tuning as black-box optimization maximizing ESS/s.