3.提供精确和近似推理方法描述,讨论与信念网络评估相关的问题,寻求标准的技术准确度和 参数敏感性指标。给出BBN的医学示例应用,如基于案例的检索和图像处理任务 Key points 1. Handling Bayesian belief networks(BBNS), appraising past and current efforts to apply these models to the medical environment. Addressing the practical considerations in the building of these models and the assumptions that must be made 2. Addressing the algorithms and tools that enable us to query BBNs. Two broad classes of queries are considered belief updating, and abductive reasoning. The former entails the re-computation of posterior probabilities in a network given some specific evidence, the latter involves calculating the optimal configuration of the bBn in order to maximize some specified criteria 3. Describing the exact and approximate inference methods. Covering special types of belief networks. Illustrating their potential usage in medicine. Looking to standard technical accuracy metrics in parametric sensitivity analysi Concluding with some example applications of BBNs in medicine, including to support case-based retrieval and image process 第8章评估及总结 知识点 1.介绍生物统计学,包括对基本概念及不同情况和假设下评估假设的统计检验。 2.引入误差和性能评估讨论,包括灵敏度、特异性和接收器操作特性分析。 3.研究设计以不同类型的测试假设形成的实验描述,并讨论变量选择和样本大小/功率计算的 过程 4.简单描述研究偏差/误差的来源,以及决策的统计工具。 5.评估基于内容的图像检索和评估(系统)可用性 Key points Introducing biostatistics including basic concepts and the statistical tests that are used to evaluate hypotheses under different circumstances and assumptions 2. Discussion of error and performance assessment is introduced, including sensitivity/specificity and receiver operative characteristic analysi 3. Study design encompasses a description of the different types of experiments that can be formed to test a hypothesis, and goes over the process of variable selection and sample size/power calculations 4. Sources of study bias/error are briefly described, as are statistical tools for decision making 5. Evaluation the content-based image retrieval; and assessing(system)usability3. 提供精确和近似推理方法描述，讨论与信念网络评估相关的问题，寻求标准的技术准确度和 参数敏感性指标。给出BBN的医学示例应用，如基于案例的检索和图像处理任务。 Key points 1. Handling Bayesian belief networks (BBNs), appraising past and current efforts to apply these models to the medical environment. Addressing the practical considerations in the building of these models and the assumptions that must be made. 2. Addressing the algorithms and tools that enable us to query BBNs. Two broad classes of queries are considered: belief updating, and abductive reasoning. The former entails the re-computation of posterior probabilities in a network given some specific evidence; the latter involves calculating the optimal configuration of the BBN in order to maximize some specified criteria. 3. Describing the exact and approximate inference methods. Covering special types of belief networks. Illustrating their potential usage in medicine. Looking to standard technical accuracy metrics in parametric sensitivity analysis. Concluding with some example applications of BBNs in medicine, including to support case-based retrieval and image processing tasks. 第8章评估及总结 知识点 1. 介绍生物统计学，包括对基本概念及不同情况和假设下评估假设的统计检验。 2. 引入误差和性能评估讨论，包括灵敏度、特异性和接收器操作特性分析。 3. 研究设计以不同类型的测试假设形成的实验描述，并讨论变量选择和样本大小/功率计算的 过程。 4. 简单描述研究偏差/误差的来源，以及决策的统计工具。 5. 评估基于内容的图像检索和评估（系统）可用性。 Key points 1. Introducing biostatistics including basic concepts and the statistical tests that are used to evaluate hypotheses under different circumstances and assumptions. 2. Discussion of error and performance assessment is introduced, including sensitivity/specificity and receiver operative characteristic analysis. 3. Study design encompasses a description of the different types of experiments that can be formed to test a hypothesis, and goes over the process of variable selection and sample size/power calculations. 4. Sources of study bias/error are briefly described, as are statistical tools for decision making. 5. Evaluation the content-based image retrieval; and assessing (system) usability