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Bringing order into bayesian-network construction

机译:令贝叶斯网络建设有序

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Among the tasks involved in building a Bayesian network, obtaining the required probabilities is generally considered the most daunting. Available data collections are often too small to allow for estimating reliable probabilities. Most domain experts, on the other hand, consider assessing the numbers to be quite demanding. Qualitative probabilistic knowledge, however, is provided more easily by experts. We propose a method for obtaining probabilities, that uses qualitative expert knowledge to constrain the probabilities learned from a small data collection. A dedicated elicitation technique is designed to support the acquisition of the qualitative knowledge required for this purpose. We demonstrate the application of our method by quantifying part of a network in the field of classical swine fever.
机译:在建立贝叶斯网络所涉及的任务中,获得所需的概率通常被认为是最艰巨的。可用的数据收集通常太小而无法估计可靠的概率。另一方面,大多数领域专家都考虑评估数字的要求很高。但是,专家更容易提供定性概率知识。我们提出了一种获取概率的方法,该方法使用定性的专家知识来约束从少量数据收集中学到的概率。专门的启发技术旨在支持为此目的所需要的定性知识的获取。我们通过量化经典猪瘟领域中网络的一部分来证明我们的方法的应用。

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