Salīdzināt metodes

	Simulācijas atbalstīta notikumu koka analīze	Uz risku balstīta notikumu koku analīze
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1970s–1990s (formalized in probabilistic risk assessment practice)	1975 (WASH-1400); risk-based integration formalized through 1980s–1990s PRA practice
Autors	H.A. Watson (Bell Telephone Laboratories, ETA origins ~1961); Monte Carlo integration of ETA developed in nuclear/aerospace PRA community 1970s–1990s	Originated in nuclear industry (US Nuclear Regulatory Commission, WASH-1400 report); risk-based framing developed through probabilistic risk assessment practice
Tips	Probabilistic risk and reliability assessment method	Risk and reliability analysis technique
Pirmavots	Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety, 94(2), 125–141.	Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 978-0521773201
Citi nosaukumi	Monte Carlo ETA, stochastic event tree analysis, simulation-enhanced ETA, probabilistic event tree simulation	Risk-based ETA, probabilistic event tree analysis, consequence-probability event tree, risk-informed ETA
Saistītās	6	4
Kopsavilkums	Simulation-assisted event tree analysis (ETA) extends classical event tree analysis by replacing fixed point-estimate branch probabilities with Monte Carlo or discrete-event simulation. This allows analysts to propagate uncertainty through every branch of the tree and obtain full probability distributions over accident sequences and system outcomes, yielding far richer risk insights than deterministic ETA alone.	Risk-based event tree analysis is a forward-looking, inductive risk assessment technique that models the consequences of an initiating event by tracing binary success/failure branches through safety barriers, then weights each outcome path by its probability to produce quantified risk estimates. Widely applied in nuclear, chemical process, aviation, and infrastructure safety engineering, it sits at the heart of probabilistic risk assessment (PRA) and supports both design decisions and regulatory compliance.