ELIABILITY is an item’s probability that it performs its required function under given conditions for a stated time interval. This characteristic is intrinsically uncertain and a stochastic variable of an object, which can be hardware, structures, software, or humans. Evaluating the reliability of these objects is a challenging problem for reliability engineers and researchers. Reliability can be evaluated using appropriate statistical inference techniques. For example, hardware reliability focuses on the research of systems and hardware and can be researched using fault tree analysis, event tree analysis, reliability block diagrams, Markov and semi-Markov models, and Petri nets. Structures are subsumed under hardware; however, we categorize them separately because they are closely related to structural mechanics principles. Structural reliability has been researched using response surface methods, first-order reliability methods, and second-order fourthmoment methods. Software reliability is defined by IEEE as the probability of failure-free software operation for a specified period of time in a specified environment. It has been researched using relevance vector regression, Gaussian processes, and the Markov-modulated Poisson process. Human reliability is the probability that an individual conducts system-required activities correctly for a specified period of time. It has been researched using ATHEANA, CREAM, and SPAR-H. Each reliability evaluation technique has its advantages and inherent disadvantages. Representing the uncertainties in the dependencies between different components or factors of the evaluated objects with many reliability evaluation methods, such as fault tree and reliability block diagram, is difficult because of the binary variable restriction [1]. Other techniques, such as Markov models and Petri nets, suffer from state space explosion problems [2]. Bayesian networks (BNs) are important probabilistic directed acyclic graphical models that can effectively characterize and analyze uncertainty, which is a problem commonly encountered in real-world domains, and handle state space explosion problems [3]. The applications of BNs has been extended to many fields involving uncertainty [4], from risk analysis [5, 6], safety engineering [7], resilience engineering [8], and fault diagnosis [9-11] to current reliability engineering, which is mainly discussed in the present work. BN-based reliability evaluation is conducted by forward (or predictive) analysis of BNs with various inference algorithms. That is, the probability of occurrence of the node that denotes the state of the evaluated object is calculated on the basis of the prior probabilities of the root nodes that denote the components or factors of the evaluated object and the conditional dependence of each node. Reliability evaluation with BNs in the hardware, structure, software, and human domains is a particularly active research area that is attracting considerable attention from reliability engineers and researchers. Several review articles have summarized previous related studies. Langseth and Portinale [12] provided a thorough literature survey on BNs applied to reliability engineering, focusing on modeling framework, including BN model construction, causal interpretation, and BN inference. Tosun et al. [13] provided a systematic review of BNs applied to software quality prediction, also focusing on BN modeling steps, namely, structure learning, parameter learning, use of tools, data characteristics, and validation. Mkrtchyan et al. [14] reviewed the use of BNs in human reliability analysis, analyzed five groups of BN applications, and identified the process of constructing BNs. In another work, Mkrtchyan et al. [15] reviewed five approaches to creating conditional probability tables and evaluated the performance of each approach.