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| Tính toán Bayes Xấp xỉ Mạnh mẽ× | Suy luận biến phân mạnh mẽ× | |
|---|---|---|
| Lĩnh vực | Bayes | Bayes |
| Họ | Bayesian methods | Bayesian methods |
| Năm ra đời≠ | 2016 | 2008-2018 |
| Người khởi xướng≠ | Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020) | Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018) |
| Loại≠ | likelihood-free inference | Robust approximate Bayesian inference |
| Công trình gốc≠ | Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692. DOI ↗ | Futami, F., Sato, I. & Sugiyama, M. (2018). Variational inference based on robust divergences. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:813-822. link ↗ |
| Tên gọi khác | Robust ABC, robust ABC inference, outlier-robust ABC, robust likelihood-free inference | RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | 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 variational inference (RVI) extends standard variational inference by replacing the Kullback-Leibler divergence with a divergence measure that is less sensitive to outliers and model misspecification — such as the beta-divergence or a Renyi-type divergence. This yields posterior approximations that remain well-behaved even when a fraction of the data departs from the assumed model. |
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