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
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian ...
62 pages, 8 figuresHamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to...
International audienceSampling from multi-dimensional and complex distributions is still a challengi...
Magnetic Hamiltonian Monte Carlo (MHMC) has been shown to provide more efficient sampling of the tar...
Hamiltonian Monte Carlo (HMC) improves the computational e ciency of the Metropolis-Hastings algorit...
Magnetic Hamiltonian Monte Carlo (MHMC) is a Markov Chain Monte Carlo method that expands on Hamilto...
Hamiltonian Monte Carlo (HMC) improves the computational efficiency of the Metropolis algorithm by r...
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of HMC which aims at sam...
Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates ...
Markov chain Monte Carlo methods have revolutionized mathematical computation and enabled statistica...
AbstractThe Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-...
The Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-dimensio...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.Cataloged fro...
Sampling from hierarchical Bayesian models is often difficult for MCMC meth-ods, because of the stro...
The Markov Chain Monte Carlo technique provides a means for drawing random samples from a target pro...
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian ...
62 pages, 8 figuresHamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to...
International audienceSampling from multi-dimensional and complex distributions is still a challengi...
Magnetic Hamiltonian Monte Carlo (MHMC) has been shown to provide more efficient sampling of the tar...
Hamiltonian Monte Carlo (HMC) improves the computational e ciency of the Metropolis-Hastings algorit...
Magnetic Hamiltonian Monte Carlo (MHMC) is a Markov Chain Monte Carlo method that expands on Hamilto...
Hamiltonian Monte Carlo (HMC) improves the computational efficiency of the Metropolis algorithm by r...
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of HMC which aims at sam...
Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates ...
Markov chain Monte Carlo methods have revolutionized mathematical computation and enabled statistica...
AbstractThe Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-...
The Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-dimensio...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2016.Cataloged fro...
Sampling from hierarchical Bayesian models is often difficult for MCMC meth-ods, because of the stro...
The Markov Chain Monte Carlo technique provides a means for drawing random samples from a target pro...
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian ...
62 pages, 8 figuresHamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to...
International audienceSampling from multi-dimensional and complex distributions is still a challengi...