This paper introduces an innovative and intuitive finite population sampling method that has been developed using a unique graphical framework. In this approach, first-order inclusion probabilities are represented as bars on a two-dimensional graph. By manipulating the positions of these bars, researchers can create a wide range of different sampling designs. This graphical visualization of sampling designs facilitates the exploration of alternative designs and may simplify certain aspects of the implementation compared to traditional mathematical algorithms. This novel approach holds significant promise for tackling complex challenges in sampling, such as achieving an optimal design. By applying a version of the greedy best-first search algorithm to this graphical approach, the potential for integrating intelligent algorithms into finite population sampling is demonstrated.

翻译：本文介绍了一种创新且直观的有限总体抽样方法，该方法基于独特的图形化框架开发。在此方法中，一阶包含概率被表示为二维图形上的条形。通过调整这些条形的位置，研究者可以创建多种不同的抽样设计。这种抽样设计的图形化可视化有助于探索替代方案，并且相较于传统的数学算法，可能简化实施的某些环节。这一新颖方法在应对抽样中的复杂挑战（例如实现最优设计）方面展现出巨大潜力。通过将一种贪心最佳优先搜索算法的变体应用于此图形化方法，我们展示了将智能算法整合到有限总体抽样中的可能性。