
@Article{skilling2019,
AUTHOR = {Skilling, John},
TITLE = {Galilean and Hamiltonian Monte Carlo},
JOURNAL = {Proceedings},
VOLUME = {33},
YEAR = {2019},
NUMBER = {1},
PAGES = {19},
URL = {https://www.mdpi.com/2504-3900/33/1/19},
ISSN = {2504-3900},
ABSTRACT = {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.},
DOI = {10.3390/proceedings2019033019}
}
