Population size estimation based on the capture-recapture experiment is an interesting problem in various fields including epidemiology, criminology, demography, etc. In many real-life scenarios, there exists inherent heterogeneity among the individuals and dependency between capture and recapture attempts. A novel trivariate Bernoulli model is considered to incorporate these features, and the Bayesian estimation of the model parameters is suggested using data augmentation. Simulation results show robustness under model misspecification and the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse real case studies on epidemiological surveillance. The results provide interesting insight on the heterogeneity and dependence involved in the capture-recapture mechanism. The methodology proposed can assist in effective decision-making and policy formulation.

翻译：基于捕获-再捕获实验的总体规模估计是流行病学、犯罪学、人口学等多个领域中的有趣问题。在许多现实场景中，个体间存在固有异质性，且捕获与再捕获尝试之间存在依赖性。本文提出一种新颖的三变量伯努利模型以纳入这些特征，并采用数据增强方法建议模型参数的贝叶斯估计。仿真结果表明，该模型在模型误设定下具有稳健性，且其性能优于现有竞争方法。该方法被应用于分析流行病学监测的实际案例研究，结果揭示了捕获-再捕获机制中涉及的异质性与依赖性的深刻见解。所提出的方法可辅助有效决策及政策制定。