Optimal Scaling of Random Walk Metropolis Algorithms with Non-Gaussian Proposals
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DOI: 10.1007/s11009-010-9176-9
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References listed on IDEAS
- Søren F. Jarner & Gareth O. Roberts, 2007. "Convergence of Heavy‐tailed Monte Carlo Markov Chain Algorithms," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 781-815, December.
- Peter Neal & Gareth Roberts, 2008. "Optimal Scaling for Random Walk Metropolis on Spherically Constrained Target Densities," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 277-297, June.
- Breyer, L. A. & Roberts, G. O., 2000. "From metropolis to diffusions: Gibbs states and optimal scaling," Stochastic Processes and their Applications, Elsevier, vol. 90(2), pages 181-206, December.
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Cited by:
- Kamatani, Kengo, 2020. "Random walk Metropolis algorithm in high dimension with non-Gaussian target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(1), pages 297-327.
- Yang, Jun & Roberts, Gareth O. & Rosenthal, Jeffrey S., 2020. "Optimal scaling of random-walk metropolis algorithms on general target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(10), pages 6094-6132.
- Jure Vogrinc & Samuel Livingstone & Giacomo Zanella, 2023. "Optimal design of the Barker proposal and other locally balanced Metropolis–Hastings algorithms," Biometrika, Biometrika Trust, vol. 110(3), pages 579-595.
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Keywords
MCMC; Cauchy distribution; Spherical distributions; Heavy tailed distributions; Random walk metropolis; Optimal scaling;All these keywords.
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