This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical models, including some that yield design-based Horvitz-Thompson estimators, the article proceeds to introduce dependence in finite populations and sets out inferential frameworks for ignorable and nonignorable responses. Multivariate dependencies using graphical models and spatial processes are discussed and some salient features of two recent analyses for spatial finite populations are presented.

翻译：本文试图为假设总体单元存在复杂依赖关系时的有限总体量的贝叶斯推断提供一些视角。首先概述了贝叶斯分层模型，包括一些产生基于设计的霍维茨-汤普森估计量的模型，进而介绍了有限总体中的依赖关系，并阐述了可忽略与不可忽略响应下的推断框架。讨论了利用图模型和空间过程处理多元依赖关系的方法，并展示了近期两项空间有限总体分析研究的若干显著特征。