विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बायेसियन फुल फैक्टोरियल डिज़ाइन× | बेयसियन प्रभाजी गुणनखंड डिज़ाइन× | |
|---|---|---|
| क्षेत्र | प्रयोगात्मक अभिकल्प | प्रयोगात्मक अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher) | 1990s |
| प्रवर्तक≠ | Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles | DuMouchel & Jones; Chipman, Hamada & Wu |
| प्रकार | Bayesian experimental design method | Bayesian experimental design method |
| मौलिक स्रोत≠ | Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304. DOI ↗ | DuMouchel, W., & Jones, B. (1994). A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model. Technometrics, 36(1), 37–47. DOI ↗ |
| उपनाम | Bayesian FFD, Bayesian complete factorial experiment, Bayesian full factorial experiment, Bayesian all-combinations design | Bayesian FFD, Bayesian screening design, Bayesian factor-screening experiment, BFF design |
| संबंधित | 3 | 3 |
| सारांश≠ | Bayesian full factorial design combines the complete combinatorial structure of classical full factorial experiments — running every combination of factor levels — with a Bayesian inferential framework that incorporates prior knowledge about factor effects and yields full posterior distributions over main effects, interactions, and model parameters, rather than point estimates and p-values. | Bayesian fractional factorial design integrates Bayesian prior information into the selection and analysis of fractional factorial experiments. Rather than running every combination of factor levels, only a carefully chosen subset of runs is executed, with Bayesian inference used to estimate effects and quantify uncertainty — even when the classical aliasing structure leaves effects confounded. |
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