Linganisha mbinu

	Muundo wa sehemu-ya-sababu ya Kibayes (Bayesian fractional factorial design)	Muundo wa Majaribio wa Bayesian
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s	1956 (foundational); formalized 1970s–1990s
Mwanzilishi	DuMouchel & Jones; Chipman, Hamada & Wu	Lindley (1956); Chaloner & Verdinelli (1995) landmark review
Aina	Bayesian experimental design method	Bayesian optimal experimental design
Chanzo asilia	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.	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.
Majina mbadala	Bayesian FFD, Bayesian screening design, Bayesian factor-screening experiment, BFF design	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE
Zinazohusiana	3	3
Muhtasari	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.	Bayesian design of experiments selects experimental runs by maximising a utility function — typically the expected information gain — computed over prior beliefs about model parameters. Unlike classical design, which optimizes algebraic criteria such as D-optimality under fixed assumptions, Bayesian DOE incorporates prior knowledge and uncertainty about the system, yielding designs that are optimal in expectation across all plausible parameter values.