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| Robuszt Approximatív Bayes-i Számítás× | Robusztus Bayes-i következtetés× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 2016 | 1984–1990 |
| Megalkotó≠ | Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020) | James O. Berger |
| Típus≠ | likelihood-free inference | Bayesian sensitivity / robustness framework |
| Alapmű≠ | Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692. DOI ↗ | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ |
| Alternatív nevek | Robust ABC, robust ABC inference, outlier-robust ABC, robust likelihood-free inference | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | Robust ABC extends standard Approximate Bayesian Computation to handle outliers, model misspecification, and sensitivity to summary statistic choice. By replacing conventional distance measures with robust alternatives — such as composite scores, trimmed statistics, or synthetic likelihoods — it protects posterior inference from being distorted by atypical observations or an imperfect simulator. | 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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