Hooten Mevin, Wikle Christopher, Schwob Michael
U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Department of Statistics Colorado State University Fort Collins 80523-1484 CO USA.
Department of Statistics University of Missouri Columbia 65211-6100 MO USA.
Int Stat Rev. 2020 Aug;88(2):441-461. doi: 10.1111/insr.12399. Epub 2020 Aug 3.
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.
当个体能够随时间被追踪时，存在多种用于研究种群动态的人口统计学统计模型。在因个体检测不完善而导致数据缺失的情况下，相关的测量误差在某些研究设计（例如涉及多次调查或重复的设计）下可以得到处理。然而，测量误差与潜在动态过程的相互作用会使基于统计主体的模型（ABM）在人口统计学中的实施变得复杂。在贝叶斯框架下，用于拟合分层人口模型的传统计算算法构建起来可能极其繁琐。因此，我们讨论了多种将统计ABM拟合到数据的方法，并展示了如何使用多阶段递归贝叶斯计算和统计模拟器来拟合模型，以减轻对ABM似然性有分析知识的需求。通过生存人口模型和新冠病毒 compartment 模型这两个例子，我们阐述了实施ABM的统计程序。我们描述的方法对从业者来说直观且易于理解，并且可以轻松并行化以提高计算效率。
