A Monte Carlo integration approach to estimating drift and minorization coefficients for Metropolis-Hastings samplers
From MaRDI portal
Publication:2244839
DOI10.1214/20-BJPS486zbMath1477.62225MaRDI QIDQ2244839
Publication date: 12 November 2021
Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)
Computational methods in Markov chains (60J22) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (2)
Approximate verification of geometric ergodicity for multiple-step Metropolis transition kernels ⋮ Estimating drift and minorization coefficients for Gibbs sampling algorithms
Uses Software
Cites Work
- Two convergence properties of hybrid samplers
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Geometric ergodicity and hybrid Markov chains
- Geometric ergodicity of Metropolis algorithms
- Inference from iterative simulation using multiple sequences
- Markov chain Monte Carlo methods for stochastic volatility models.
- Rates of convergence of the Hastings and Metropolis algorithms
- A computational procedure for estimation of the mixing time of the random-scan Metropolis algorithm
- Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
- Simulation Run Length Control in the Presence of an Initial Transient
- Convergence of Slice Sampler Markov Chains
- On the geometric ergodicity of hybrid samplers
- Bayesian Model Selection in Finite Mixtures by Marginal Density Decompositions
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Gibbs Sampler Convergence Criteria
- Estimation of risk contributions with MCMC
- Adaptive importance sampling in monte carlo integration
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: A Monte Carlo integration approach to estimating drift and minorization coefficients for Metropolis-Hastings samplers
