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Found 8 papers, 2 papers with code
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo
We present a novel and flexible framework for localized tuning of Hamiltonian Monte Carlo samplers by sampling the algorithm's tuning parameters conditionally based on the position and momentum at each step.
Randomized Runge-Kutta-Nyström
We present 5/2- and 7/2-order $L^2$-accurate randomized Runge-Kutta-Nystr\"om methods to approximate the Hamiltonian flow underlying various non-reversible Markov chain Monte Carlo chains including unadjusted Hamiltonian Monte Carlo and unadjusted kinetic Langevin chains.
Unadjusted Hamiltonian MCMC with Stratified Monte Carlo Time Integration
A novel randomized time integrator is suggested for unadjusted Hamiltonian Monte Carlo (uHMC) in place of the usual Verlet integrator; namely, a stratified Monte Carlo (sMC) integrator which involves a minor modification to Verlet, and hence, is easy to implement.
Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlo
We provide quantitative upper bounds on the total variation mixing time of the Markov chain corresponding to the unadjusted Hamiltonian Monte Carlo (uHMC) algorithm.
Couplings for Andersen Dynamics
Andersen dynamics is a standard method for molecular simulations, and a precursor of the Hamiltonian Monte Carlo algorithm used in MCMC inference.
Coupling and Convergence for Hamiltonian Monte Carlo
Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w. r. t.
Geometric integrators and the Hamiltonian Monte Carlo method
This paper surveys in detail the relations between numerical integration and the Hamiltonian (or hybrid) Monte Carlo method (HMC).
Probability Numerical Analysis Computation Methodology
Stochastic Variational Integrators
This paper presents a continuous and discrete Lagrangian theory for stochastic Hamiltonian systems on manifolds.
Probability 65Cxx; 37Jxx
