摘要翻译：
提出了一种基于马尔可夫链蒙特卡罗(MCMC)方法训练的贝叶斯框架的粗糙集模型训练方法。先验概率是根据好的粗糙集模型规则较少的先验知识构造的。Markov链Monte Carlo抽样是在粗糙集粒度空间中进行抽样，采用Metropolis算法作为验收准则。对所提出的方法进行了测试，以估计给定人口数据的艾滋病毒风险。实验结果表明，该方法的平均正确率为58%，正确率变化幅度可达66%。此外，贝叶斯粗糙集给出了估计的HIV状态的概率，以及描述人口统计参数如何驱动HIV风险的语言规则。
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英文标题：
《Bayesian approach to rough set》
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作者：
Tshilidzi Marwala and Bodie Crossingham
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.
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