Salīdzināt metodes

	Kontrolkarte ar beisiskajiem datiem	Vadības diagramma
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	Formally developed in the 1990s–2000s; roots in Shewhart (1924)	1924 (first use); 1931 (seminal book)
Autors	Ulrich Menzefricke and others building on Shewhart (1924) and Bayesian inference (Bayes, 1763)	Walter A. Shewhart (Bell Labs)
Tips	Statistical process monitoring / quality control	Statistical monitoring and control technique
Pirmavots	Menzefricke, U. (2002). On the evaluation of control chart limits based on predictive distributions. Communications in Statistics — Theory and Methods, 31(8), 1423–1440.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.
Citi nosaukumi	Bayesian SPC chart, Bayesian monitoring chart, posterior control chart, Bayesian Shewhart chart	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Saistītās	6	6
Kopsavilkums	A Bayesian control chart integrates prior knowledge about a process — such as historical mean and variance — with incoming measurement data to produce dynamically updated control limits. Unlike classical Shewhart charts that fix limits from a Phase-I baseline, Bayesian charts update the posterior distribution of process parameters after each sample, yielding limits that adapt to accumulated evidence and are better calibrated under small sample sizes or non-stationary processes.	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.