Mixing of Metropolis-adjusted Markov chains via couplings: the high acceptance regime

Continuous-time Markov processes on general state spaces (60J25) Discrete-time Markov processes on general state spaces (60J05) Diffusion processes (60J60)

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Tuning diagonal scale matrices for HMC

Cites Work

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Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
Subgeometric rates of convergence in Wasserstein distance for Markov chains
Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
Markov chains and stochastic stability
Asymptotic coupling and a general form of Harris' theorem with applications to stochastic delay equations
Hybrid Monte Carlo on Hilbert spaces
Lectures on convex optimization
General state space Markov chains and MCMC algorithms
A note on Metropolis-Hastings kernels for general state spaces
Computable bounds for geometric convergence rates of Markov chains
Exponential convergence of Langevin distributions and their discrete approximations
A geometric interpretation of the Metropolis-Hastings algorithm.
One-shot coupling for certain stochastic recursive sequences.
Estimates of the determinant of a perturbed identity matrix
Analysis of a nonreversible Markov chain sampler.
Rates of convergence of the Hastings and Metropolis algorithms
Irreducibility and geometric ergodicity of Hamiltonian Monte Carlo
Two-scale coupling for preconditioned Hamiltonian Monte Carlo in infinite dimensions
Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates
Mixing time guarantees for unadjusted Hamiltonian Monte Carlo
Couplings for Andersen dynamics
On sampling from a log-concave density using kinetic Langevin diffusions
High-dimensional MCMC with a standard splitting scheme for the underdamped Langevin diffusion
High-dimensional Bayesian inference via the unadjusted Langevin algorithm
On the geometric ergodicity of Hamiltonian Monte Carlo
Couplings and quantitative contraction rates for Langevin dynamics
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
Error bounds for Metropolis-Hastings algorithms applied to perturbations of Gaussian measures in high dimensions
Subgeometric rates of convergence of Markov processes in the Wasserstein metric
Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise.
Quantitative contraction rates for Markov chains on general state spaces
Coupling and convergence for Hamiltonian Monte Carlo
Yet Another Look at Harris’ Ergodic Theorem for Markov Chains
Geometric Numerical Integration
Pathwise accuracy and ergodicity of metropolized integrators for SDEs
The Markov chain Monte Carlo revolution
Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
Markov Chains
Quantitative Harris-type theorems for diffusions and McKean–Vlasov processes
Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
Nonasymptotic mixing of the MALA algorithm
Unbiased Hamiltonian Monte Carlo with couplings
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates
Unbiased Markov Chain Monte Carlo Methods with Couplings
Equation of State Calculations by Fast Computing Machines
Geometric integrators and the Hamiltonian Monte Carlo method
The Andersen thermostat in molecular dynamics
Monte Carlo sampling methods using Markov chains and their applications
Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities
On explicit \(L^2\)-convergence rate estimate for underdamped Langevin dynamics
Complexity results for MCMC derived from quantitative bounds
Convergence of unadjusted Hamiltonian Monte Carlo for mean-field models

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