Confronta i metodi

	Controllo Statistico di Processo Bayesiano	Progettazione Bayesiana di Esperimenti
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1950s (foundations); formalized 1990s–2000s	1956 (foundational); formalized 1970s–1990s
Ideatore	Various (Girshick & Rubin 1952 early signal detection; Menzefricke 2002 Bayesian control chart framework)	Lindley (1956); Chaloner & Verdinelli (1995) landmark review
Tipo	Bayesian process monitoring technique	Bayesian optimal experimental design
Fonte seminale	Menzefricke, U. (2002). On the evaluation of control chart factors for monitoring the process mean and variance. Journal of Quality Technology, 34(2), 167–178.	Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.
Alias	Bayesian SPC, Bayesian process monitoring, B-SPC, Bayesian control charting	Bayesian DOE, Bayesian optimal design, Bayesian experimental design, BDE
Correlati	5	3
Sintesi	Bayesian Statistical Process Control (Bayesian SPC) extends classical SPC by replacing fixed, frequentist control limits with a probabilistic framework that incorporates prior knowledge about the process. Rather than waiting for a run of points to exceed a pre-set 3-sigma boundary, Bayesian SPC continuously updates the probability that the process has shifted given the incoming data, enabling earlier and more informed detection of out-of-control states while formally accounting for uncertainty in process parameters.	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.