We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal--Wishart distributions to derive our metrics for discrete and Gaussian domains.

翻译：我们从先前的知识和统计数据的组合中研究拜伊斯人网络的学习方法。 特别是, 我们统一了我们在去年的会议上为离散域和高斯域提出的方法。 我们得出了适合这两个域通用的贝伊斯人评分标准。 然后, 我们结合众所周知的Drichlet和正常- Wishart分布的统计事实, 来得出我们对离散域和高斯域的衡量标准。