论文标题
随机网络上的贝叶斯模型选择
Bayesian Model Selection on Random Networks
论文作者
论文摘要
考虑了有关其功能的随机网络模型上模型选择的一般贝叶斯框架。目的是开发一种贝叶斯模型选择方法，以比较可推断这些网络实现的不同拟合（不一定是嵌套的模型）。关于比较的随机网络模型的标准是通过贝叶斯因素制定的，并使用最使用的损失函数进行了惩罚。参数化在不同的空间中是不同的。为了克服这个问题，我们在可观察的空间方面纳入并编码复杂性的不同方面。因此，给定功能的一系列值，提取了网络实现。提出的原则方法基于查找随机网络模型，以便保留了感兴趣的功能和模型复杂性之间的合理权衡，从而避免了过度拟合的问题。
A general Bayesian framework for model selection on random network models regarding their features is considered. The goal is to develop a principle Bayesian model selection approach to compare different fittable, not necessarily nested, models for inference on those network realisations. The criterion for random network models regarding the comparison is formulated via Bayes factors and penalizing using the mostwidely used loss functions. Parametrizations are different in different spaces. To overcome this problem we incorporate and encode different aspects of complexities in terms of observable spaces. Thus, given a range of values for a feature, network realisationsare extracted. The proposed principle approach is based on finding random network models, such that a reasonable trade off between the interested feature and the complexity of the model is preserved, avoiding overfitting problems.
