Bayesian Networks is a model that efficiently represents knowledge and probabilistic inference and is a popular graphics decision-making analysis tool.
贝叶斯网络是在不确定性环境下有效的知识表示方式和概率推理模型，是一种流行的图形决策化分析工具。
In order to discover probabilistic decision rules in preferential multiple attribute decision system with incomplete information, an extension of the rough sets model is proposed in the paper.
为了从有偏好信息但信息不完全的多属性决策系统中获取概率决策规则，提出一种新的不完全信息的多属性粗糙决策分析方法。
The results show that this method can enhance the steadiness in statistical decision-making and bring about the optimum fitting of probabilistic model of geotechnical parameters.
研究结果表明：该方法可以增强统计决策中的稳健性，实现统计意义上概率分布类型的优化。
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