Accelerating adaptation in the adaptive Metropolis–Hastings random walk algorithm
From MaRDI portal
Publication:6075126
DOI10.1111/ANZS.12344zbMath1521.62011OpenAlexW3213669456MaRDI QIDQ6075126
Publication date: 20 October 2023
Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/anzs.12344
Computational methods in Markov chains (60J22) Computational methods for problems pertaining to statistics (62-08) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)
Cites Work
- Unnamed Item
- Unnamed Item
- Adaptive proposal distribution for random walk Metropolis algorithm
- On the stability and ergodicity of adaptive scaling Metropolis algorithms
- On adaptive Markov chain Monte Carlo algorithms
- Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms
- The random walk Metropolis: linking theory and practice through a case study
- Exponential convergence of Langevin distributions and their discrete approximations
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Optimal scaling for various Metropolis-Hastings algorithms.
- Optimal scaling of the independence sampler: theory and practice
- Weight-preserving simulated tempering
- Automatically tuned general-purpose MCMC via new adaptive diagnostics
- Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process
- Accelerating parallel tempering: Quantile tempering algorithm (QuanTA)
- Equation of State Calculations by Fast Computing Machines
- Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
- Monte Carlo sampling methods using Markov chains and their applications
- A Stochastic Approximation Method
- Computer Vision
- An adaptive Metropolis algorithm
This page was built for publication: Accelerating adaptation in the adaptive Metropolis–Hastings random walk algorithm