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| Robust Monte Carlo-simulering× | Robust Bayesiansk Inferens× | |
|---|---|---|
| Fagområde | Bayesiansk | Bayesiansk |
| Familie | Bayesian methods | Bayesian methods |
| Oprindelsesår≠ | 1990s–2000s | 1984–1990 |
| Ophavsperson≠ | Saltelli, Rubinstein, and the uncertainty-quantification community | James O. Berger |
| Type≠ | Robust simulation / uncertainty quantification | Bayesian sensitivity / robustness framework |
| Oprindelig kilde≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975 | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ |
| Aliasser | robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes |
| Relaterede | 6 | 6 |
| Resumé≠ | Robust Monte Carlo simulation extends standard Monte Carlo by explicitly accounting for uncertainty in input distributions, model structure, or parameter assumptions. Rather than assuming a single fixed probability distribution for each input, the analyst considers a family of plausible distributions and evaluates how sensitive the output is to those choices, yielding conclusions that hold across a range of reasonable assumptions. | 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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