Method evidence record
Robust Markov chain Monte Carlo
Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates.
Source record
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Robust Markov Chain Monte Carlo Sampling
Taxonomic method record · bayesian / bayesian
- Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71. · DOI 10.1214/154957804100000024
- Barp, A., Kennedy, C., Durmus, A. & Girolami, M. (2022). Targeted separation and convergence with kernel discrepancies. arXiv preprint. · URL
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