块模型边际似然的模拟一致估计
Simulation-consistent Estimation of the Marginal Likelihood for Block Models
浏览论文内容
中文总结 AI 辅助
提出计算块模型边际似然的方法，该估计器基于MCMC样本，模拟一致、渐近正态、方差有限、对标签切换不变且计算高效，通过模拟研究和应用于COP28社交网络数据集进行评估。
中文摘要 AI 辅助
我们提出了一种计算块模型边际似然的方法。所提出的估计器从马尔可夫链蒙特卡罗（MCMC）样本计算边际似然，即使数据集大小固定也是模拟一致的。此外，它是渐近正态的，具有有限方差，对标签切换不变，并且即使对于具有任意大量组件的模型也能高效计算。我们通过在可解析获得真实边际似然的设置下进行模拟研究来评估该方法。最后，我们将该方法应用于基于2023年联合国气候变化大会（COP28）的社交网络数据集并讨论所得见解。
英文摘要
We propose a methodology for computing marginal likelihoods for block models. The proposed estimator computes the marginal likelihood from Markov chain Monte Carlo (MCMC) samples and is simulation-consistent, even when the size of the dataset is fixed. Moreover, it is asymptotically normal, of finite variance, invariant to label switching and can be computed efficiently, even for models with an arbitrarily large number of components. We evaluate the method through simulation studies in settings where the true marginal likelihood is available analytically. Finally, we apply the approach to a social network dataset based on the 2023 United Nations Climate Change Conference (COP28) and discuss the resulting insights.
