Comparison of MCMC Adaptation Schemes: A Preliminary Empirical Study-Reference-Cited by-同舟云学术

Comparison of MCMC Adaptation Schemes: A Preliminary Empirical Study

Published:2023-07-15 Issue: Volume: Page:
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Container-title:Proceedings of the Companion Conference on Genetic and Evolutionary Computation
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Author:

Liu Bowen¹^ORCID,Milgo Edna¹²^ORCID,Ronoh Nixon¹³^ORCID,Bernard Manderick¹^ORCID

Affiliation:

1. Computer Science Department/AI Lab, Vrije Universiteit Brussel, Brussels, Belgium

2. Department of Physics, Mathematics and Computer Science/School of Science and Aerospace Studies, Moi University, Eldoret, Kenya

3. Department of Physics, Mathematics and Computer Science/School of Science and Aerospace Studies, Moi University, Eldoret, BE, Kenya

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583133.3590707

Reference11 articles.

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2. Stephen P. Brooks and Andrew Gelman . 1998 . General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics 7-4 (1998), 434--455. Stephen P. Brooks and Andrew Gelman. 1998. General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics 7-4 (1998), 434--455.

3. Andrew Gelman , Walter R Gilks , and Gareth O Roberts . 1997. Weak convergence and optimal scaling of random walk Metropolis algorithms. The annals of applied probability 7, 1 ( 1997 ), 110--120. Andrew Gelman, Walter R Gilks, and Gareth O Roberts. 1997. Weak convergence and optimal scaling of random walk Metropolis algorithms. The annals of applied probability 7, 1 (1997), 110--120.

4. Inference from Iterative Simulation Using Multiple Sequences

5. Heikki Haario , Eero Saksman , and Johanna Tamminen . 1999. Adaptive proposal distribution for random walk Metropolis algorithm. Computational Statistics 14-3 ( 1999 ), 375--395. Heikki Haario, Eero Saksman, and Johanna Tamminen. 1999. Adaptive proposal distribution for random walk Metropolis algorithm. Computational Statistics 14-3 (1999), 375--395.