Comparer des méthodes

	Analyse des causes profondes bayésienne	Analyse Bayésienne d'Arbre de Défaillance
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	2001 (BFTA mapping); Bayesian networks: 1988
Auteur d'origine	Rooted in Pearl's Bayesian network theory (Judea Pearl, 1988); applied to RCA in process/reliability engineering from the 1990s onward	Andrea Bobbio, Luca Portinale et al. (mapping FTA to Bayesian networks); Judea Pearl (Bayesian networks)
Type	Probabilistic causal inference method	Probabilistic reliability / safety analysis
Source fondatrice	Pourret, O., Naim, P., & Marcot, B. (Eds.). (2008). Bayesian Networks: A Practical Guide to Applications. Wiley. ISBN: 978-0470060308	Bobbio, A., Portinale, L., Minichino, M., & Ciancamerla, E. (2001). Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety, 71(3), 249–260.
Alias	Bayesian RCA, Bayesian causal analysis, probabilistic root cause analysis, BN-RCA	BFTA, Bayesian FTA, Bayesian network fault tree, probabilistic fault tree 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 Fault Tree Analysis (BFTA) extends classical fault tree analysis by converting the fault tree structure into an equivalent Bayesian network, enabling probabilistic inference in both forward (prediction) and backward (diagnosis) directions. This integration allows analysts to update failure probability estimates with observed evidence, quantify uncertainty explicitly, and identify the most probable root causes of a top-level system failure.