We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics

翻译：我们提出了一种用于分析相互作用种群时空观测数据的模型比较方法的概念验证。我们的模型变体是一组结构相似的贝叶斯网络。这些网络采用不同的噪声或条件概率分布来描述种群内部的相互作用，每种分布对应一种特定的相互作用机制。为确定哪些分布最能准确表征潜在机制，我们检验了各贝叶斯网络相对于观测数据的准确性。我们将该系统应用于观测进行接合作用的细菌种群——接合是一种水平基因转移方式，微生物通过物理接触与邻近细胞共享遗传物质。由于该过程的随机性，评估影响接合作用的细胞特异性因素通常较为困难。我们的方法为深入理解这一过程提供了新途径。我们在三项实验测试中分别比较了八种模型变体，并使用两种不同度量指标对其进行排序。