Сравнение методов

	Байесовский статистический контроль процессов	Shewhart Charts
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1950s (foundations); formalized 1990s–2000s	1924 (first use); 1931 (seminal book)
Автор метода	Various (Girshick & Rubin 1952 early signal detection; Menzefricke 2002 Bayesian control chart framework)	Walter A. Shewhart (Bell Labs)
Тип	Bayesian process monitoring technique	Statistical monitoring and control technique
Основополагающий источник	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.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.
Другие названия	Bayesian SPC, Bayesian process monitoring, B-SPC, Bayesian control charting	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Связанные	5	6
Сводка	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.	A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.