We study Hamiltonian Monte Carlo (HMC) samplers based on splitting the Hamiltonian H as H0(θ , p)+U1(θ ), where H0 is quadratic and U1 small. We show that, in general, such samplers suffer from stepsize stability restrictions similar to those of algorithms based on the standard leapfrog integrator. The restrictions may be circumvented by preconditioning the dynamics. Numerical experiments show that, when the H0(θ , p) + U1(θ ) splitting is combined with preconditioning, it is possible to construct samplers far more efficient than standard leapfrog HMC.Funding for open access charge: CRUE-Universitat Jaume
We introduce a new class of Hamiltonian Monte Carlo (HMC) algorithm called Conservative Hamiltonian ...
The modified Hamiltonian Monte Carlo (MHMC) methods, i.e., importance sampling methods that use modi...
62 pages, 8 figuresHamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to...
We study Hamiltonian Monte Carlo (HMC) samplers based on splitting the Hamiltonian H as H0(θ,p)+U1(θ...
Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two popular sampling approa...
We propose a splitting Hamiltonian Monte Carlo (SHMC) algorithm, which can be computationally effici...
We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting ” the Ha...
Splitting schemes are numerical integrators for Hamiltonian problems that may advantageously replace...
We construct integrators to be used in Hamiltonian (or Hybrid) Monte Carlo sampling. The new integra...
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with...
Sampling from hierarchical Bayesian models is often difficult for MCMC meth-ods, because of the stro...
[EN] We construct integrators to be used in Hamiltonian (or Hybrid) Monte Carlo sampling. The new in...
The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computat...
In this paper, we discuss an extension of the Split Hamiltonian Monte Carlo (Split HMC) method for G...
154 p.The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in co...
We introduce a new class of Hamiltonian Monte Carlo (HMC) algorithm called Conservative Hamiltonian ...
The modified Hamiltonian Monte Carlo (MHMC) methods, i.e., importance sampling methods that use modi...
62 pages, 8 figuresHamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to...
We study Hamiltonian Monte Carlo (HMC) samplers based on splitting the Hamiltonian H as H0(θ,p)+U1(θ...
Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two popular sampling approa...
We propose a splitting Hamiltonian Monte Carlo (SHMC) algorithm, which can be computationally effici...
We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting ” the Ha...
Splitting schemes are numerical integrators for Hamiltonian problems that may advantageously replace...
We construct integrators to be used in Hamiltonian (or Hybrid) Monte Carlo sampling. The new integra...
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with...
Sampling from hierarchical Bayesian models is often difficult for MCMC meth-ods, because of the stro...
[EN] We construct integrators to be used in Hamiltonian (or Hybrid) Monte Carlo sampling. The new in...
The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computat...
In this paper, we discuss an extension of the Split Hamiltonian Monte Carlo (Split HMC) method for G...
154 p.The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in co...
We introduce a new class of Hamiltonian Monte Carlo (HMC) algorithm called Conservative Hamiltonian ...
The modified Hamiltonian Monte Carlo (MHMC) methods, i.e., importance sampling methods that use modi...
62 pages, 8 figuresHamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo method that allows to...