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A general approach to Bayesian networks for the interpretation of evidence.

机译:贝叶斯网络解释证据的一般方法。

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摘要

Bayesian networks (BNs) are mathematically and statistically rigorous techniques for handling uncertainty. The field of forensic science has recently attributed increased attention to the many advantages of this graphical method for assisting the evaluation of scientific evidence. However, the majority of contributions that relate to this topic restrict themselves to the presentation of already "constructed" BNs, and often, only a few explanations are given as to how one obtains a specific BN structure for a given problem. Based on several examples, the present paper will therefore attempt to explain in more detail some guiding considerations that might be helpful for the elicitation of appropriate structures for BNs.
机译:贝叶斯网络(BNs)是用于处理不确定性的数学和统计严格技术。法医科学领域最近将更多的注意力归功于这种图形方法在协助评估科学证据方面的许多优势。但是,与该主题相关的大多数文稿将自己局限于已经“构建”的BN的表示,并且通常仅给出关于如何为给定问题获得特定BN结构的几种解释。因此,基于几个示例,本论文将尝试更详细地解释一些指导性考虑,这些指导性考虑可能有助于引发BN的适当结构。

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