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| Robust Bayesian Network× | Inferensi Bayesian Robust× | |
|---|---|---|
| Bidang | Bayesian | Bayesian |
| Keluarga | Bayesian methods | Bayesian methods |
| Tahun asal≠ | 1991-2000 | 1984–1990 |
| Pencetus≠ | Fabio Cozman (credal networks); Peter Walley (imprecise probabilities) | James O. Berger |
| Tipe≠ | probabilistic graphical model with set-valued probabilities | Bayesian sensitivity / robustness framework |
| Sumber perintis≠ | Cozman, F. G. (2000). Credal networks. Artificial Intelligence, 120(2), 199-233. DOI ↗ | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ |
| Alias | RBN, credal network, imprecise Bayesian network, sensitivity analysis in Bayesian networks | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes |
| Terkait≠ | 5 | 6 |
| Ringkasan≠ | A Robust Bayesian Network extends a classical Bayesian network by replacing each precise conditional probability table with a set of allowable probability distributions — called a credal set. Instead of a single probability for each query, inference returns a range of probabilities, honestly reflecting uncertainty about the model's numeric parameters while preserving the interpretable directed-acyclic-graph structure. | 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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