เปรียบเทียบวิธี

	การวิเคราะห์แผนภูมิต้นไม้เหตุการณ์ด้วยการจำลองสถานการณ์	การวิเคราะห์แผนภูมิต้นไม้เหตุการณ์แบบเบย์
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1970s–1990s (formalized in probabilistic risk assessment practice)	ETA: 1960s–1970s; Bayesian extension: 1990s–2000s
ผู้ริเริ่ม	H.A. Watson (Bell Telephone Laboratories, ETA origins ~1961); Monte Carlo integration of ETA developed in nuclear/aerospace PRA community 1970s–1990s	H.E. Watson (Bell Labs, fault tree); ETA formalized via US Nuclear Regulatory Commission; Bayesian extension developed in reliability and risk engineering communities
ประเภท	Probabilistic risk and reliability assessment method	Probabilistic risk and reliability analysis technique
แหล่งต้นตำรับ	Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety, 94(2), 125–141.	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.
ชื่อเรียกอื่น	Monte Carlo ETA, stochastic event tree analysis, simulation-enhanced ETA, probabilistic event tree simulation	Bayesian ETA, B-ETA, Probabilistic Event Tree Analysis, Bayesian Inductive Risk Model
ที่เกี่ยวข้อง	6	5
สรุป	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.	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.