Linganisha mbinu

	Uchanganuzi wa Mti wa Hitilafu (FTA)	Mtandao wa Bayesian	Uchambuzi wa Kiaminifu wa Kitakwimu
Nyanja	Utegemewa	Mbinu za Bayes	Utegemewa
Familia	Process / pipeline	Bayesian methods	Regression model
Mwaka wa asili	1981	1988	1998
Mwanzilishi	Vesely et al. (US NRC Fault Tree Handbook)	Judea Pearl	William Meeker & Luis Escobar
Aina	Deductive top-down failure analysis	Probabilistic graphical model	Parametric lifetime modeling
Chanzo asilia	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook (NUREG-0492). U.S. Nuclear Regulatory Commission.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Meeker, W. Q., & Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley. ISBN: 978-0-471-14328-4
Majina mbadala	FTA, Fault Tree Method, Top-Down Reliability Analysis, Hata Ağacı Analizi	Bayes network, belief network, probabilistic graphical model, directed graphical model	Life Data Analysis, Survival Analysis (Engineering), Time-to-Failure Analysis, Güvenilirlik Analizi
Zinazohusiana	3	4	3
Muhtasari	Fault Tree Analysis (FTA) is a top-down, deductive reliability method that begins with an undesired top-level failure event and systematically traces backward through chains of contributing causes using Boolean logic gates (AND, OR). First formalized by Watson at Bell Telephone Laboratories in 1961 and later standardized by Vesely, Goldberg, Roberts, and Haasl in the landmark 1981 NRC Fault Tree Handbook, FTA has become a cornerstone of quantitative risk assessment in nuclear, aerospace, and industrial safety engineering.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.	Statistical reliability analysis models the time-to-failure of components, systems, or products using parametric lifetime distributions fitted to observed or censored failure data. Formalized comprehensively by William Q. Meeker and Luis A. Escobar in their 1998 Wiley monograph, the framework integrates maximum likelihood estimation, censoring mechanisms, and distributional diagnostics to produce probability-of-failure curves, hazard rates, and quantile estimates that support design, warranty, and maintenance decisions.