On the convergence of stochastic approximations under a subgeometric ergodic Markov dynamic
From MaRDI portal
Publication:2044347
DOI10.1214/21-EJS1827zbMath1466.60136OpenAlexW3034791360WikidataQ114060467 ScholiaQ114060467MaRDI QIDQ2044347
Stéphanie Allassonnière, Vianney Debavelaere, Stanley Durrleman
Publication date: 9 August 2021
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/21-ejs1827
Discrete-time Markov processes on general state spaces (60J05) Stochastic programming (90C15) Stochastic approximation (62L20)
Cites Work
- Unnamed Item
- Unnamed Item
- Limit theorems for some adaptive MCMC algorithms with subgeometric kernels. II
- Limit theorems for some adaptive MCMC algorithms with subgeometric kernels
- Construction of Bayesian deformable models via a stochastic approximation algorithm: a convergence study
- Maximum likelihood estimation in nonlinear mixed effects models
- Convergence and robustness of the Robbins-Monro algorithm truncated at randomly varying bounds
- \(V\)-subgeometric ergodicity for a Hastings-Metropolis algorithm
- Geometric ergodicity of Metropolis algorithms
- Convergence of a stochastic approximation version of the EM algorithm
- Practical drift conditions for subgeometric rates of convergence.
- Properties of the stochastic approximation EM algorithm with mini-batch sampling
- A stochastic algorithm for probabilistic independent component analysis
- Polynomial ergodicity of Markov transition kernels.
- Bayesian Mixed Effect Atlas Estimation with a Diffeomorphic Deformation Model
- Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
- Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
- Stochastic Approximation for Nonexpansive Maps: Application to Q-Learning Algorithms
- Markov Chains
- Coupling a stochastic approximation version of EM with an MCMC procedure
- The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
- Stability of Stochastic Approximation under Verifiable Conditions
- Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
- A Stochastic Approximation Method
- An adaptive Metropolis algorithm