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：本文试图为当总体单位表现出复杂依赖性时的有限总体量贝叶斯推断提供一些视角。从贝叶斯层次模型（包括能够产生基于设计的霍维茨-汤普森估计量的模型）的概述入手，文章进而介绍有限总体中的依赖性，并阐述了可忽略响应与不可忽略响应的推断框架。讨论了使用图模型和空间过程的多变量依赖性，并呈现了最近针对空间有限总体进行的两项分析中的一些显著特征。