A fresh Take on ‘Barker Dynamics’ for MCMC

DOI10.1007/978-3-030-98319-2_8arXiv2012.09731OpenAlexW3111752196MaRDI QIDQ6154290

Giacomo Zanella, Unnamed Author, Samuel Livingstone

Publication date: 14 February 2024

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.09731

zbMATH Keywords

Markov chainsLangevin dynamicsMCMCMetropolis-HastingsBarker dynamics

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05)

Cites Work

• Information-geometric Markov chain Monte Carlo methods using diffusions
• General state space Markov chains and MCMC algorithms
• A note on Metropolis-Hastings kernels for general state spaces
• Exponential convergence of Langevin distributions and their discrete approximations
• Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
• Some properties of skew-symmetric distributions
• The tamed unadjusted Langevin algorithm
• User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
• Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
• Optimum Monte-Carlo sampling using Markov chains
• Optimal Scaling of Discrete Approximations to Langevin Diffusions
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Kinetic energy choice in Hamiltonian/hybrid Monte Carlo
• Informed Proposals for Local MCMC in Discrete Spaces
• Equation of State Calculations by Fast Computing Machines
• MCMC for Imbalanced Categorical Data
• Monte Carlo sampling methods using Markov chains and their applications

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