Combining mirror-reflection proposals with a Langevin gradient drift yields an MCMC method that the authors show to be faster per unit time than HMC/NUTS on Bayesian GLMMs.
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Efficient Mirror-type Kernels for the Metropolis-Hastings Algorithm
Combining mirror-reflection proposals with a Langevin gradient drift yields an MCMC method that the authors show to be faster per unit time than HMC/NUTS on Bayesian GLMMs.