摘要翻译：
将贝叶斯模型平均(BMA)方法应用于新生儿睡眠脑电图脑成熟度评估。在理论上，这种方法提供了决策不确定性的最准确的评估。然而，现有的BMA技术在缺乏一些先验信息的情况下提供了有偏差的评估，从而能够在合理的时间内详细地探索模型参数空间。细节上的缺乏导致从后验分布中取样不成比例。对于脑成熟度的脑电评估，由于缺乏脑电特征重要性的信息，BMA结果可能存在偏差。本文探讨了如何利用脑电特征的后验信息来减少非比例采样对BMA性能的负面影响。我们使用从睡眠新生儿记录的脑电数据来测试所提出的BMA技术的效率。
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英文标题：
《Feature Importance in Bayesian Assessment of Newborn Brain Maturity from
  EEG》
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作者：
L. Jakaite, V. Schetinin, and C. Maple
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最新提交年份：
2010
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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：
  The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence of some prior information enabling to explore model parameter space in details within a reasonable time. The lack in details leads to disproportional sampling from the posterior distribution. In case of the EEG assessment of brain maturity, BMA results can be biased because of the absence of information about EEG feature importance. In this paper we explore how the posterior information about EEG features can be used in order to reduce a negative impact of disproportional sampling on BMA performance. We use EEG data recorded from sleeping newborns to test the efficiency of the proposed BMA technique.
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PDF链接：
https://arxiv.org/pdf/1002.4522