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	Analisi Ibrida degli Alberi di Eventi	Analisi Bayesiana degli Alberi di Eventi
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1990s–2000s (as extensions to classical ETA developed from the 1960s)	ETA: 1960s–1970s; Bayesian extension: 1990s–2000s
Ideatore	Multiple contributors; hybrid extensions emerged from the reliability and safety engineering community	H.E. Watson (Bell Labs, fault tree); ETA formalized via US Nuclear Regulatory Commission; Bayesian extension developed in reliability and risk engineering communities
Tipo	Probabilistic risk and safety assessment technique	Probabilistic risk and reliability analysis technique
Fonte seminale	Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 978-0521773201	Bearfield, G., & Marsh, W. (2005). Generalising event trees using Bayesian networks with a case study of train derailment. In G. Windeknecht et al. (Eds.), Proceedings of the 13th Safety-Critical Systems Symposium. Springer.
Alias	Hybrid ETA, Integrated Event Tree Analysis, Combined Event Tree Analysis, Fuzzy-Bayesian Event Tree Analysis	Bayesian ETA, B-ETA, Probabilistic Event Tree Analysis, Bayesian Inductive Risk Model
Correlati	6	5
Sintesi	Hybrid Event Tree Analysis (Hybrid ETA) extends classical Event Tree Analysis by integrating complementary methods — such as Bayesian networks, fuzzy set theory, or Monte Carlo simulation — to overcome ETA's limitations in handling uncertainty, dependency between events, and sparse data. It is applied in safety-critical industries to model accident sequences and quantify outcome probabilities with greater fidelity than standalone ETA.	Bayesian Event Tree Analysis (B-ETA) is a quantitative risk assessment method that extends classical event tree analysis by incorporating Bayesian inference to assign and update branch probabilities. Starting from an initiating event, it maps sequences of successes and failures through safety barriers, using prior distributions and observed evidence to produce posterior outcome probabilities. Widely used in nuclear safety, process industries, and system reliability engineering.