Methoden vergleichen

	Bayesian Design of Experiments	Design of Experiments
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1956 (foundational); formalized 1970s–1990s	1935
Urheber	Lindley (1956); Chaloner & Verdinelli (1995) landmark review	Ronald A. Fisher
Typ	Bayesian optimal experimental design	Experimental planning framework
Wegweisende Quelle	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Aliasnamen	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE	DOE, experimental design, factorial experimentation, planned experimentation
Verwandt	3	3
Zusammenfassung	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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.