Latent Class Models (LCMs) are used to cluster multivariate categorical data,
commonly used to interpret survey responses. We propose a novel Bayesian model
called the Equivalence Set Restricted Latent Class Model (ESRLCM). This model
identifies clusters who have common item response probabilities, and does so
more generically than traditional restricted latent attribute models. We verify
the identifiability of ESRLCMs, and demonstrate the effectiveness in both
simulations and real-world applications.

我们提出了一种称为等价集限制潜在类别模型 (ESRLCM) 的新型贝叶斯模型，用于聚类多元分类数据，并且相对于传统的限制潜在属性模型具有更广泛的适用性。我们验证了 ESRLCM 的可识别性，并在模拟和实际应用中证明了其有效性。

等价集限制潜类模型 (ESRLCM)

Equivalence Set Restricted Latent Class Models (ESRLCM)

We present a selective review of statistical modeling of dynamic networks. We
focus on models with latent variables, specifically, the latent space models
and the latent class models (or stochastic blockmodels), which investigate both
the observed features and the unobserved structure of networks. We begin with
an overview of the static models, and then we introduce the dynamic extensions.
For each dynamic model, we also discuss its applications that have been studied
in the literature, with the data source listed in Appendix. Based on the
review, we summarize a list of open problems and challenges in dynamic network
modeling with latent variables.

本文回顾了动态网络的统计建模方法。我们重点介绍具有潜在变量的模型，特别是潜在空间模型和潜在类模型，旨在研究网络的观察特征和未观察结构。同时，我们还总结了这些动态模型在文献中研究的应用，并列出了数据来源。在此基础上，我们概括了动态网络建模中潜在变量存在的几个开放性问题和挑战。