Wasserstein-based methods for convergence complexity analysis of MCMC with applications
From MaRDI portal
Publication:2117437
DOI10.1214/21-AAP1673MaRDI QIDQ2117437
Publication date: 21 March 2022
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.08826
couplingrandom mappinggeometric ergodicitydrift conditionminorization conditionhigh dimensional inference
Related Items
Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models, Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions, Spectral telescope: convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure, Exact convergence analysis for metropolis–hastings independence samplers in Wasserstein distances, Convergence rate bounds for iterative random functions using one-shot coupling
Cites Work
- Unnamed Item
- Unnamed Item
- A study of variable selection using \(g\)-prior distribution with ridge parameter
- Convergence properties of Gibbs samplers for Bayesian probit regression with proper priors
- On the computational complexity of high-dimensional Bayesian variable selection
- 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
- Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors
- Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains
- Ricci curvature of Markov chains on metric spaces
- Geometric ergodicity of Gibbs and block Gibbs samplers for a hierarchical random effects model
- Computable bounds for geometric convergence rates of Markov chains
- Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
- Locally contractive iterated function systems
- Bounds on regeneration times and convergence rates for Markov chains
- One-shot coupling for certain stochastic recursive sequences.
- Renewal theory and computable convergence rates for geometrically erdgodic Markov chains
- High-dimensional MCMC with a standard splitting scheme for the underdamped Langevin diffusion
- On the limitations of single-step drift and minorization in Markov chain convergence analysis
- Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions: continuous dynamics
- Convergence complexity analysis of Albert and Chib's algorithm for Bayesian probit regression
- High-dimensional Bayesian inference via the unadjusted Langevin algorithm
- A concise course on stochastic partial differential equations
- Subgeometric rates of convergence of Markov processes in the Wasserstein metric
- Matrix concentration inequalities via the method of exchangeable pairs
- Coupling and convergence for Hamiltonian Monte Carlo
- Locally contracting iterated functions and stability of Markov chains
- Markov Chains and De-initializing Processes
- Yet Another Look at Harris’ Ergodic Theorem for Markov Chains
- Convergence in the Wasserstein Metric for Markov Chain Monte Carlo Algorithms with Applications to Image Restoration
- Markov Chains and Stochastic Stability
- Variable Selection in Regression Mixture Modeling for the Discovery of Gene Regulatory Networks
- Propriety of posterior distribution for dichotomous quantal response models
- Markov Chains
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Convergence Rates and Asymptotic Standard Errors for Markov Chain Monte Carlo Algorithms for Bayesian Probit Regression
- MCMC for Imbalanced Categorical Data
- Bayesian Analysis of Binary and Polychotomous Response Data
- Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm
