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首页> 外文期刊>高分子論文集 >Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks
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Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks

机译：用于从数据中学习多项式分布的不精确概率模型。在学习网络中的应用

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This paper considers the problem of learning multinomial distributions from a sample of independent observations. The Bayesian approach usually assumes a prior Dirichlet distribution about the probabilities of the different possible values. However, there is no consensus on the parameters of this Dirichlet distribution. Here, it will be shown that this is not a simple problem, providing examples in which different selection criteria are reasonable. To solve it the Imprecise Dirichlet Model (IDM) was introduced. But this model has important drawbacks, as the problems associated to learning from indirect observations. As an alternative approach, the Imprecise Sample Size Dirichlet Model (ISSDM) is introduced and its properties are studied. The prior distribution over the parameters of a multinomial distribution is the basis to learn Bayesian networks using Bayesian scores. Here, we will show that the ISSDM can be used to learn imprecise Bayesian networks, also called credal networks when all the distributions share a common graphical structure. Some experiments are reported on the use of the ISSDM to learn the structure of a graphical model and to build supervised classifiers.
机译：本文考虑了从独立观察样本中学习多项式分布的问题。贝叶斯方法通常假设关于不同可能值的概率的先验Dirichlet分布。但是，关于这种狄利克雷分布的参数尚无共识。在这里，将显示出这不是一个简单的问题，并提供了不同选择标准合理的示例。为了解决这个问题，引入了不精确的狄利克雷模型（IDM）。但是，该模型具有重要的缺点，因为与从间接观察中学习有关的问题。作为一种替代方法，引入了不精确样本量狄利克雷模型（ISSDM）并研究了其性质。多项式分布参数上的先验分布是使用贝叶斯得分学习贝叶斯网络的基础。在这里，我们将证明，当所有分布共享相同的图形结构时，ISSDM可用于学习不精确的贝叶斯网络，也称为credal网络。关于使用ISSDM来学习图形模型的结构并建立监督分类器的报道，进行了一些实验。

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