On the limitations of single-step drift and minorization in Markov chain convergence analysis
From MaRDI portal
Publication:2240862
DOI10.1214/20-AAP1628zbMath1476.60113arXiv2003.09555OpenAlexW3201118272MaRDI QIDQ2240862
Publication date: 4 November 2021
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.09555
renewal theoryconvergence ratecouplinggeometric ergodicityoptimal boundhigh-dimensional inferencequantitative boundGaussian autoregressive process
Related Items
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling, Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models, Exact convergence analysis of the independent Metropolis-Hastings algorithms, Convergence of Position-Dependent MALA with Application to Conditional Simulation in GLMMs, Explicit bounds for spectral theory of geometrically ergodic Markov kernels and applications, Exact convergence analysis for metropolis–hastings independence samplers in Wasserstein distances, On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models, Wasserstein-based methods for convergence complexity analysis of MCMC with applications
Cites Work
- Unnamed Item
- Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
- Quantitative bounds for Markov chain convergence: Wasserstein and total variation distances
- Asymptotic coupling and a general form of Harris' theorem with applications to stochastic delay equations
- Geometric bounds for eigenvalues of Markov chains
- General state space Markov chains and MCMC algorithms
- Simulated annealing via Sobolev inequalities
- Computable bounds for geometric convergence rates of Markov chains
- Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
- Bounds on regeneration times and convergence rates for Markov chains
- One-shot coupling for certain stochastic recursive sequences.
- Elementary bounds on Poincaré and log-Sobolev constants for decomposable Markov chains
- Renewal theory and computable convergence rates for geometrically erdgodic Markov chains
- Practical drift conditions for subgeometric rates of convergence.
- Markov chains for exploring posterior distributions. (With discussion)
- Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions
- Convergence complexity analysis of Albert and Chib's algorithm for Bayesian probit regression
- High-dimensional Bayesian inference via the unadjusted Langevin algorithm
- Subgeometric rates of convergence of Markov processes in the Wasserstein metric
- Quantitative contraction rates for Markov chains on general state spaces
- Coupling and convergence for Hamiltonian Monte Carlo
- Geometric L2 and L1 convergence are equivalent for reversible Markov chains
- Bounds on the L 2 Spectrum for Markov Chains and Markov Processes: A Generalization of Cheeger's Inequality
- Long time behavior of Markov processes
- Markov Chains and Stochastic Stability
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
- Rates of convergence of stochastically monotone and continuous time Markov models
- Markov Chains
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Geometric Convergence Rates for Stochastically Ordered Markov Chains
- Scaling Limits for the Transient Phase of Local Metropolis–Hastings Algorithms
- Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities