Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale

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Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale

10 October 2018

Christian P. Robert

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Papers citing "Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale"

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Title
A Direct Importance Sampling-based Framework for Rare Event Uncertainty Quantification in Non-Gaussian Spaces Elsayed M. Eshra Konstantinos G. Papakonstantinou Hamed Nikbakht 57 0 0 23 May 2024
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo Nawaf Bou-Rabee Bob Carpenter Milo Marsden 150 7 0 23 Apr 2024
The Apogee to Apogee Path Sampler Chris Sherlock S. Urbas Matthew Ludkin 99 6 0 15 Dec 2021
Entropy-based adaptive Hamiltonian Monte Carlo Marcel Hirt Michalis K. Titsias P. Dellaportas BDL 101 7 0 27 Oct 2021
Focusing on Difficult Directions for Learning HMC Trajectory Lengths Pavel Sountsov Matt Hoffman 95 10 0 22 Oct 2021
Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions Chirag Modi A. Barnett Bob Carpenter 98 14 0 01 Oct 2021
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates T. S. Kleppe 56 12 0 04 May 2020
Functional probabilistic programming for scalable Bayesian modelling Jonathan Law D. Wilkinson 15 1 0 06 Aug 2019
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients Yuansi Chen Raaz Dwivedi Martin J. Wainwright Bin Yu 67 102 0 29 May 2019