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Open AccessProceedings

Galilean and Hamiltonian Monte Carlo

by John Skilling
Maximum Entropy Data Consultants Ltd., CB4 1XE Kenmare, Ireland
Presented at the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Garching, Germany, 30 June–5 July 2019.
Proceedings 2019, 33(1), 19; https://doi.org/10.3390/proceedings2019033019
Published: 5 December 2019
Galilean Monte Carlo (GMC) allows exploration in a big space along systematic trajectories, thus evading the square-root inefficiency of independent steps. Galilean Monte Carlo has greater generality and power than its historical precursor Hamiltonian Monte Carlo because it discards second-order propagation under forces in favour of elementary force-free motion. Nested sampling (for which GMC was originally designed) has similar dominance over simulated annealing, which loses power by imposing an unnecessary thermal blurring over energy.
Keywords: Nested sampling; simulated annealing; Hamiltonian Monte Carlo; Galilean Monte Carlo Nested sampling; simulated annealing; Hamiltonian Monte Carlo; Galilean Monte Carlo
MDPI and ACS Style

Skilling, J. Galilean and Hamiltonian Monte Carlo. Proceedings 2019, 33, 19.

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