Irreversible samplers from jump and continuous Markov processes

DOI10.1007/s11222-018-9802-xzbMath1430.62060arXiv1608.05973OpenAlexW3106335019MaRDI QIDQ2329758

Yanyan Li

Publication date: 18 October 2019

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.05973

zbMATH Keywords

Markov chain Monte Carlojump processesBayesian inferenceMetropolis-HastingsHamiltonian Monte Carloirreversible samplers

Mathematics Subject Classification ID

Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Jump processes on discrete state spaces (60J74)

Related Items

On Irreversible Metropolis Sampling Related to Langevin Dynamics ⋮ Applying kriging proxies for Markov chain Monte Carlo in reservoir simulation ⋮ Non-reversible guided Metropolis kernel ⋮ Hamiltonian-Assisted Metropolis Sampling ⋮ Efficient Bayesian Computation for Low-Photon Imaging Problems ⋮ On the convergence time of some non-reversible Markov chain Monte Carlo methods ⋮ Is there an analog of Nesterov acceleration for gradient-based MCMC?

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
• The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
• Variance reduction using nonreversible Langevin samplers
• Improving the convergence of reversible samplers
• Non-reversible Metropolis-Hastings
• Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion
• Equi-energy sampler with applications in statistical inference and statistical mechanics
• Relation of a new interpretation of stochastic differential equations to Itô process
• Accelerating diffusions
• Irreversible Monte Carlo algorithms for efficient sampling
• Semigroups of linear operators and applications to partial differential equations
• Accelerating Gaussian diffusions
• Langevin diffusions and Metropolis-Hastings algorithms
• Thermodynamics of the general diffusion process: time-reversibility and entropy production
• Strong consistency of multivariate spectral variance estimators in Markov chain Monte Carlo
• Analysis of a nonreversible Markov chain sampler.
• Bayesian learning for neural networks
• Markovian perturbation, response and fluctuation dissipation theorem
• A piecewise deterministic scaling limit of lifted Metropolis-Hastings in the Curie-Weiss model
• Acceleration of convergence to equilibrium in Markov chains by breaking detailed balance
• On the notion(s) of duality for Markov processes
• Langevin diffusions and the Metropolis-adjusted Langevin algorithm
• Attaining the optimal Gaussian diffusion acceleration
• Accelerating reversible Markov chains
• Adaptive Thermostats for Noisy Gradient Systems
• Lifting Markov chains to speed up mixing
• Irreversible Langevin samplers and variance reduction: a large deviations approach
• Long-Run Accuracy of Variational Integrators in the Stochastic Context
• Practical Markov Chain Monte Carlo
• Universal ideal behavior and macroscopic work relation of linear irreversible stochastic thermodynamics
• Hypocoercivity
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Multivariate output analysis for Markov chain Monte Carlo
• Stochastic Processes and Applications
• Equation of State Calculations by Fast Computing Machines
• Fluctuations in Markov Processes
• A decomposition of irreversible diffusion processes without detailed balance
• Existence and construction of dynamical potential in nonequilibrium processes without detailed balance
• Monte Carlo sampling methods using Markov chains and their applications
• Monte Carlo strategies in scientific computing
• A function space HMC algorithm with second order Langevin diffusion limit