Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully-connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

翻译：机理统计模型常用于研究生物过程的流动。例如，在景观遗传学中，目标是推断控制种群基因流动的空间机制。现有的景观遗传学统计方法未能考虑数据中的时间依赖性，且计算成本可能过高。我们通过贝叶斯层次二元模型推断机制，该模型能很好地适应大规模数据集，并同时考虑空间和时间依赖性。我们构建了一个包含时空数据的全连接网络用于二元模型，并采用归一化复合似然函数来刻画时空依赖结构。我们开发了一种二元模型以涵盖物理统计模型中常见的物理机制，并将我们的方法应用于古人类DNA数据，以推断影响青铜时代欧洲人类迁徙的机制。