Tripuraneni, N and Rowland, M and Ghahramani, Z and Turner, R (2017) Magnetic hamiltonian Monte Carlo. 34th International Conference on Machine Learning, ICML 2017, 7. pp. 5292-5312.
Full text not available from this repository.Abstract
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits non-canonical Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3 dimensions a subset of the dynamics map onto the mechanics of a charged particle coupled to a magnetic field. We establish a theoretical basis for the use of non-canonical Hamiltonian dynamics in MCMC, and construct a symplectic, leapfrog-like integrator allowing for the implementation of magnetic HMC. Finally, we exhibit several examples where these non-canonical dynamics can lead to improved mixing of magnetic HMC relative to ordinary HMC.
Item Type: | Article |
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Uncontrolled Keywords: | stat.ML stat.ML |
Subjects: | UNSPECIFIED |
Divisions: | Div F > Computational and Biological Learning |
Depositing User: | Cron Job |
Date Deposited: | 17 Jul 2017 20:14 |
Last Modified: | 15 Apr 2021 05:52 |
DOI: |