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| Bayesowski plan Boxa-Behnkena× | Pełny czynnikowy plan eksperymentu× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina≠ | Process / pipeline | Hypothesis test |
| Rok powstania≠ | 1960 (BBD); Bayesian integration ~1990s–2000s | 1926 |
| Twórca≠ | Box & Behnken (classical BBD, 1960); Bayesian extension developed by multiple authors in response surface literature | R. A. Fisher |
| Typ≠ | Bayesian response surface experimental design | Parametric factorial experiment |
| Źródło pierwotne≠ | Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475. DOI ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Inne nazwy | Bayesian BBD, Bayesian RSM Box-Behnken, Bayesian three-level design, BBD with Bayesian optimization | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | Bayesian Box-Behnken Design combines the classical Box-Behnken three-level design structure with Bayesian statistical inference to fit and optimize response surface models. It uses mid-edge and center points to efficiently estimate a second-order polynomial response surface while incorporating prior knowledge about model parameters and propagating uncertainty through to predictions and optimal factor settings. The approach is widely applied in engineering process optimization and formulation studies. | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
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