Efficiency of delayed-acceptance random walk Metropolis algorithms

Author:

Sherlock Chris1,Thiery Alexandre H.2,Golightly Andrew3

Affiliation:

1. Department of Mathematics and Statistics, Lancaster University

2. Department of Statistics and Applied Probability, National University of Singapore

3. School of Mathematics, Statistics and Physics, Newcastle University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Survey of Monte Carlo Methods for Noisy and Costly Densities With Application to Reinforcement Learning and ABC;International Statistical Review;2024-05-17

2. Accelerating inference for stochastic kinetic models;Computational Statistics & Data Analysis;2023-09

3. Optimal scaling of MCMC beyond Metropolis;Advances in Applied Probability;2022-12-16

4. Application of Hierarchical Optimal Random Walk Algorithm in Data Specification of College Sports Training Intelligent Platform;2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC);2022-08-17

5. Variance Bounding of Delayed-Acceptance Kernels;Methodology and Computing in Applied Probability;2021-11-22

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