Summary A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent‐based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi‐stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID‐19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.

中文翻译：

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基于主体的人口统计模型的统计实现


摘要 当可以随时间跟踪个体时，存在多种人口统计模型用于研究人口动态。如果由于个体检测不完善而导致数据丢失，则可以在某些研究设计（例如涉及多次调查或重复的研究设计）下解决相关的测量误差。然而，测量误差和潜在动态过程的相互作用可能会使基于统计代理的人口统计学模型（ABM）的实施变得复杂。在贝叶斯环境中，用于拟合分层人口统计模型的传统计算算法构建起来可能非常麻烦。因此，我们讨论了将统计 ABM 拟合到数据的各种方法，并演示了如何使用多级递归贝叶斯计算和统计模拟器来拟合模型，从而减轻对 ABM 可能性分析知识的需要。我们使用两个例子（生存的人口统计模型和 COVID-19 的隔室模型）来说明实施 ABM 的统计程序。我们描述的方法对于从业者来说是直观且易于理解的，并且可以轻松并行化以提高计算效率。