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Hamiltonian Monte Carlo samplers have become standard algorithms for MCMCimplementations, as opposed to more basic versions, but they still require someamount of tuning and calibration. Exploiting the U-turn criterion of the NUTSalgorithm (Hoffman and Gelman, 2014), we propose a version of HMC that relieson the distribution of the integration time of the associated leapfrogintegrator. Using in addition the primal-dual averaging method for tuning thestep size of the integrator, we achieve an essentially calibration free versionof HMC. When compared with the original NUTS on several benchmarks, thisalgorithm exhibits a significantly improved efficiency.

Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale