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| Robust Hamiltonsk Monte Carlo× | Robust Bayesiansk Inferens× | |
|---|---|---|
| Fagområde | Bayesiansk | Bayesiansk |
| Familie | Bayesian methods | Bayesian methods |
| Oprindelsesår≠ | 2010s–2020s | 1984–1990 |
| Ophavsperson≠ | Livingstone, Zanella and related researchers building on Duane et al. (1987) | James O. Berger |
| Type≠ | Robust MCMC sampler | Bayesian sensitivity / robustness framework |
| Oprindelig kilde≠ | Livingstone, S. & Zanella, G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B, 84(2), 496–523. DOI ↗ | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ |
| Aliasser | Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes |
| Relaterede≠ | 4 | 6 |
| Resumé≠ | Robust Hamiltonian Monte Carlo (Robust HMC) is a family of extensions to standard HMC designed to maintain geometric ergodicity and sampling efficiency when the posterior has heavy tails, strong curvature variation, or near-degenerate geometry. By modifying the kinetic energy, mass matrix, or proposal mechanism, these methods ensure reliable exploration of difficult posteriors that defeat the standard NUTS/HMC sampler. | Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions. |
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