## 2 Literature Review  
The reliability of a product is affected by multiple criteria, such as technical maturity, structural complexity, quality stability and so on. Multiple Criteria Decision Making (MCDM) has achieved great success as a vital and popular research branch of decision-making theory [17]. The analytic hierarchy process (AHP) is often used to determine the weight of each criterion as a utility-based MCDM technique. Simultaneously, [PERSON] [18] proposed the best-worst method (BWM) to improve the efficiency of calculations and obtain more reliable results, and it has been widely used for risk assessment [19; 20] and in supply chain management [21; 22]. Furthermore, [PERSON] and [PERSON] [23] introduced Bayesian theory into BWM; their approach finds the optimal weights of a set of criteria based on a group of experts. However, we not only need to obtain the weight of each criterion but also have experts quantitatively evaluate the reliability of the product against each criterion. A group of experts should be invited to evaluate the product to reduce subjectivity, and thus an expert system should be established that can collect and integrate evaluations to better reflect experts' judgments. Some objective data need to be simultaneously aggregated with the experts' judgments. Information can be integrated from behavioral and mathematical perspectives. The behavioral approach is established to generate some type of group consensus among experts, often encouraging them to communicate with each other and share their opinions. In contrast to the behavioral approach, the mathematical approach maintains independence and prevents the biases that may arise due to their extensive interaction, and the outcome of the assessment is not controlled or even determined by a single expert [24]. In this paper, we only discuss the mathematical approach. [PERSON] et al. [25] aggregated a small set of experts by a nonlinear function and proposed a linear feedback iteration scheme to solve the nonlinear function. [PERSON] et al. [26] established a multi-source information fusion technique that employed evidence theory, probability theory and information entropy--an example is used to elaborate the operation mechanism. [PERSON] et al. [27] developed a physics of failure-based reliability prediction method by multi-source information fusion, and this method is used to predict the reliability of the turbine blades of aero engines. Bayesian inference is a common method used to pool different information together and has been used for fault diagnosis [28; 29], reliability evaluation [30; 31] and residual life prediction [32; 33]. These models and methods supplement the necessary foundation for the reliability evaluation of new products. [PERSON] et al. [34] developed a hierarchical Bayesian model to evaluate the reliability of complex systems in the early stage when no system-level fault data are available. [PERSON] et al. [30] proposed a Bayesian model updating approach and constructed an information integration toolkit and information transition toolkit. [PERSON] and [PERSON] [35] generalized a method for evaluating the reliability of a system; this method combined information from multiple sources and multiple levels of the system by a Bayesian network approach. [PERSON] et al. [36] introduced the Bayesian modeling method (BMM) for system reliability assessment with multi-source information, which includes the subsystem and system information. Under such a scenario, it is important to develop a well-established framework or methodology that can incorporate multiple inconsistent priors into a unified model, especially when the reliability test data are limited in the early stages of PD. In this paper, we only obtain limited failure data due to time and cost constraints. If quantitative failure data are inappropriate or inadequate, only qualitative data, such as expert opinions described in words, can be used to assess the safety and reliability of the system.