The network influence model is a model for binary outcome variables that accounts for dependencies between outcomes for units that are relationally tied. The basic influence model was previously extended to afford a suite of new dependence assumptions and because of its relation to traditional Markov random field models it is often referred to as the auto logistic actor-attribute model (ALAAM). We extend on current approaches for fitting ALAAMs by presenting a comprehensive Bayesian inference scheme that supports testing of dependencies across subsets of data and the presence of missing data. We illustrate different aspects of the procedures through three empirical examples: masculinity attitudes in an all-male Australian school class, educational progression in Swedish schools, and unemployment among adults in a community sample in Australia.

中文翻译：

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社会影响的贝叶斯分析

网络影响模型是二元结果变量的模型，它解释了相关联的单元的结果之间的依赖性。基本影响模型之前经过扩展以提供一套新的依赖性假设，并且由于它与传统马尔可夫随机场模型的关系，它通常被称为自动逻辑参与者属性模型 (ALAAM)。我们通过提出一个全面的贝叶斯推理方案来扩展当前拟合 ALAAM 的方法，该方案支持测试数据子集之间的依赖关系和缺失数据的存在。我们通过三个实证示例说明程序的不同方面：澳大利亚全男性学校班级的男子气概态度、瑞典学校的教育进展以及澳大利亚社区样本中成年人的失业率。