The importance Markov chain
From MaRDI portal
Publication:6204189
DOI10.1016/J.SPA.2024.104316arXiv2207.08271OpenAlexW4321322195MaRDI QIDQ6204189
Hugo Marival, Randal Douc, Christian Robert, Charly Andral
Publication date: 27 March 2024
Published in: Stochastic Processes and their Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2207.08271
Computational methods in Markov chains (60J22) Bayesian inference (62F15) Monte Carlo methods (65C05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Numerical analysis or methods applied to Markov chains (65C40)
Cites Work
- Unnamed Item
- Unnamed Item
- The pseudo-marginal approach for efficient Monte Carlo computations
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- Markov chain importance sampling with applications to rare event probability estimation
- An overview of robust Bayesian analysis. (With discussion)
- Self-regenerative Markov chain Monte Carlo with adaptation
- Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it
- Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
- A vanilla Rao-Blackwellization of Metropolis-Hastings algorithms
- On convergence of properly weighted samples to the target distribution
- Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis
- Bayesian Models for Astrophysical Data
- Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
- Dynamic weighting in Monte Carlo and optimization
- A Theory for Dynamic Weighting in Monte Carlo Computation
- Markov Chains
- Markov Chain Importance Sampling—A Highly Efficient Estimator for MCMC
- Equation of State Calculations by Fast Computing Machines
- Limit theorems for weighted samples with applications to sequential Monte Carlo methods
- Monte Carlo sampling methods using Markov chains and their applications
- The Monte Carlo Method
This page was built for publication: The importance Markov chain