Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected‐Posterior Prior and on the Power‐Expected‐Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein‐signaling data, which wieldy confirms existing results in the scientific literature.

中文翻译：

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图形结构学习的客观方法

图形模型用于通过图表来表示变量之间的条件独立性关系，图表的结构通常是未知的，必须通过手头的数据来推断。我们提出了一种理论上合理的Objective Bayes程序用于图形模型选择。我们的方法基于期望后验优先级和幂期望后验优先级。我们使用默认的不正确的先验作为建议方法的输入，并提出贝叶斯因子和后验优势的计算有效近似。在具有不同数量的节点和样本大小的各种模拟场景中，我们证明了我们的方法与当前基准相比具有很高的竞争力，甚至更好。我们还将讨论蛋白质信号数据的应用，这将证明科学文献中已有的结果。