美国论文代写：我们生活中的数据
除了校准，还有一种更科学的方法是贝叶斯方法。在这里，专家的选择必须被解释，它必须被记录，以便所有的判断都支持一个或另一个原因(Macgill, Fewtrell和Kay, 2000)。必须采取合理的步骤，查明和消除或尽量减少利用专家对所使用的诱出方法的判断所产生的任何偏见。另一个办法是培训专家提供正式意见，并向专家提供所有有关细节。
美国论文代写：我们生活中的数据
同样，群体决策支持系统(GDSS)在支持专家判断程序和调查利益相关者在评估风险时的反应方面有着巨大的前景。对于专家判断程序，重要的是在GDSS中实施以下几个方面:
图形布局，以协助启发，
说明了一个鼓励参数要求的接口过程
一个聚合“引擎”计算并显示目标措施的聚合概率分布
对于利益相关者的反馈，支持评分/投票的调查方法可以在许多GDSSs中使用。
建议将风险评价的质量认证工作分配给独立的同事。非偏倚表示同侪组的参与者在风险评估研究中没有既得利益。
美国论文代写：我们生活中的数据
Apart from calibration, one more scientific approach would be the Bayesian approach. Here in the choice of experts has to be explained and it has to be documented so that all the judgments are supported by one reason or the other (Macgill, Fewtrell and Kay, 2000). There has to be reasonable steps taken for identifying and eliminating or minimizing any kind of bias that has come from the utilization of expert judgment on the elicitation methods used. Another approach would be training the experts for providing formal opinions and making all the relevant details available to the experts.
美国论文代写：我们生活中的数据
Similarly, Group Decision Support Systems (GDSS) have a huge prospective to support the expert judgment procedure and survey the stakeholders’ reaction in assessing risk. For the expert judgment procedure, it is important to implement the below aspects in the GDSS:
graphical layouts to assist elicitation,
an interface encouraging the parameter requirement procedure explained, and
an aggregation ‘engine’ calculating and showing the aggregate probability distribution of the aimed measure
For the stakeholder feedback, the surveying methods which support scoring/voting are accessible in lots of GDSSs.
Moreover, it is suggested that the quality authentication of risk evaluation should be allotted to an independent colleagues. The non-bias denotes that the partakers in the peer group have no vested interests in the risk evaluation study.
