Optimal scaling for partially updating MCMC algorithms
From MaRDI portal
Publication:997939
DOI10.1214/105051605000000791zbMath1127.60021arXivmath/0607054OpenAlexW2088198131MaRDI QIDQ997939
Publication date: 8 August 2007
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/math/0607054
Computational methods in Markov chains (60J22) Central limit and other weak theorems (60F05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)
Related Items
Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits, Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions, Hierarchical models: local proposal variances for RWM-within-Gibbs and MALA-within-Gibbs, A Dirichlet form approach to MCMC optimal scaling, Mixing of MCMC algorithms, Optimal scaling of random-walk Metropolis algorithms on general target distributions, Optimal scaling of MCMC beyond Metropolis, The random walk Metropolis: linking theory and practice through a case study, Statistical analysis of an endemic disease from a capture–recapture experiment, Optimal scaling of random walk Metropolis algorithms with non-Gaussian proposals, Optimal scaling for random walk Metropolis on spherically constrained target densities, MALA-within-Gibbs Samplers for High-Dimensional Distributions with Sparse Conditional Structure, Weak convergence of Metropolis algorithms for non-I.I.D. target distributions, Weak convergence and optimal tuning of the reversible jump algorithm, Optimal scaling of random walk Metropolis algorithms with discontinuous target densities, Hierarchical models and tuning of random walk Metropolis algorithms, Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition, Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234, Estimating and Projecting Trends in HIV/AIDS Generalized Epidemics Using Incremental Mixture Importance Sampling, Optimal scaling of Metropolis algorithms: Heading toward general target distributions, Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers, Solution of the inverse scattering problem from inhomogeneous media using affine invariant sampling
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Langevin diffusions and Metropolis-Hastings algorithms
- Optimal scaling for various Metropolis-Hastings algorithms.
- From Metropolis to diffusions: Gibbs states and optimal scaling.
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
