Comparer des méthodes

	Analyse des causes profondes bayésienne	Bayesian failure mode and effects analysis
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	1990s–2000s
Auteur d'origine	Rooted in Pearl's Bayesian network theory (Judea Pearl, 1988); applied to RCA in process/reliability engineering from the 1990s onward	Extension of classical FMEA (MIL-STD-1629, 1974) with Bayesian inference formalised in reliability literature from the 1990s onward
Type	Probabilistic causal inference method	Probabilistic reliability and risk analysis
Source fondatrice	Pourret, O., Naim, P., & Marcot, B. (Eds.). (2008). Bayesian Networks: A Practical Guide to Applications. Wiley. ISBN: 978-0470060308	Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203–213.
Alias	Bayesian RCA, Bayesian causal analysis, probabilistic root cause analysis, BN-RCA	Bayesian FMEA, probabilistic FMEA, B-FMEA, Bayesian risk priority analysis
Apparentées	6	5
Résumé	Bayesian Root Cause Analysis (Bayesian RCA) integrates Bayesian network theory with structured root cause investigation to quantify the probability that each candidate cause is responsible for an observed failure or undesired event. Unlike deterministic RCA methods, it propagates uncertainty through the causal graph, updates beliefs as evidence accumulates, and ranks competing hypotheses by posterior probability — providing a principled, auditable basis for corrective action.	Bayesian FMEA extends the classical Failure Mode and Effects Analysis framework by replacing fixed point-estimate risk scores with probability distributions, allowing prior engineering knowledge and observed failure data to be formally combined through Bayes' theorem. The result is a probabilistic Risk Priority Number (RPN) that reflects uncertainty in severity, occurrence, and detectability ratings rather than masking it with single consensus values.