Bayesian Root Cause Analysis
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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Pourret, O., Naim, P., & Marcot, B. (Eds.). (2008). Bayesian Networks: A Practical Guide to Applications. Wiley. · ISBN 978-0470060308
- Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671–682. · DOI 10.1016/j.engappai.2010.06.002
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.