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Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: an offline randomized solution
We introduce a Hamiltonian Monte Carlo (HMC) methodology based on an offline empirical calibration of randomized leapfrog parameters. The approach, referred to as eHMC, where \textit{e} stands for empirical, leverages importance sampling to construct an empirical distribution on discretization param
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