Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm
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Publication:5963544
DOI10.1007/s11222-014-9511-zzbMath1332.62282OpenAlexW2078038392MaRDI QIDQ5963544
Publication date: 22 February 2016
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11222-014-9511-z
Markov processes: estimation; hidden Markov models (62M05) Numerical analysis or methods applied to Markov chains (65C40) Markov processes (60Jxx)
Related Items (13)
Limit behavior of the invariant measure for Langevin dynamics ⋮ Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models ⋮ Quantitative contraction rates for Markov chains on general state spaces ⋮ Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions ⋮ Convergence of Position-Dependent MALA with Application to Conditional Simulation in GLMMs ⋮ Exact convergence analysis for metropolis–hastings independence samplers in Wasserstein distances ⋮ Perturbation theory for Markov chains via Wasserstein distance ⋮ On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models ⋮ On the geometric ergodicity of Hamiltonian Monte Carlo ⋮ Convergence rate bounds for iterative random functions using one-shot coupling ⋮ Mixing rates for Hamiltonian Monte Carlo algorithms in finite and infinite dimensions ⋮ Stochastic Gradient MCMC for State Space Models ⋮ Wasserstein-based methods for convergence complexity analysis of MCMC with applications
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