Non-reversible guided Metropolis kernel

DOI10.1017/jpr.2022.109zbMath1520.65004arXiv2005.05584MaRDI QIDQ6116753

Kengo Kamatani, Xiaolin Song

Publication date: 16 August 2023

Published in: Journal of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.05584

zbMATH Keywords

Markov chain Monte CarloHaar measurereversibilityBayesian inference

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05) Numerical analysis or methods applied to Markov chains (65C40)

Cites Work

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• 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
• Non-reversible Metropolis-Hastings
• Irreversible Monte Carlo algorithms for efficient sampling
• A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data
• Monte Carlo maximum likelihood estimation for discretely observed diffusion processes
• Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions
• Estimation for diffusion processes from discrete observation
• Comparison theorems for reversible Markov chains
• Exponential convergence of Langevin distributions and their discrete approximations
• Geometric ergodicity and hybrid Markov chains
• RcppArmadillo: accelerating R with high-performance C++ linear algebra
• Geometric MCMC for infinite-dimensional inverse problems
• Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution
• Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
• Analysis of a nonreversible Markov chain sampler.
• Geometric ergodicity and the spectral gap of non-reversible Markov chains
• Peskun-Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario
• Irreversible samplers from jump and continuous Markov processes
• A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms
• Optimal tuning of the hybrid Monte Carlo algorithm
• Geometric L² and ^{L1 convergence are equivalent for reversible Markov chains}
• Asymptotic theory for non-linear least squares estimator for diffusion processes
• Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
• Approximate discrete-time schemes for statistics of diffusion processes
• MCMC METHODS FOR DIFFUSION BRIDGES
• Gamma processes
• Optimal Scaling of Discrete Approximations to Langevin Diffusions
• Generalised Gibbs sampler and multigrid Monte Carlo for Bayesian computation
• Parameter Expansion for Data Augmentation
• Ergodicity of Markov chain Monte Carlo with reversible proposal
• Multivariate T-Distributions and Their Applications
• Auxiliary Gradient-Based Sampling Algorithms
• A discrete bouncy particle sampler
• Two Metropolis--Hastings Algorithms for Posterior Measures with Non-Gaussian Priors in Infinite Dimensions
• The Bayesian Choice
• On random- and systematic-scan samplers
• A function space HMC algorithm with second order Langevin diffusion limit
• Bayesian computation: a summary of the current state, and samples backwards and forwards
• MCMC methods for functions: modifying old algorithms to make them faster
• Hug and hop: a discrete-time, nonreversible Markov chain Monte Carlo algorithm