Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.

极值理论（EVT）旨在研究概率分布的尾部，以测量和量化最大和最小的极端事件。在河流流量数据中，河流的极端水位可能与流入它的相邻河流的水位有关。在这种类型的数据中，一个位置的洪水很常见是由距该位置数十或数百公里的富裕河流的非常大的流量引起的。从这个意义上说，一种有趣的方法是考虑使用条件模型来估计多元模型。受这个想法的启发，我们提出了一个贝叶斯模型来描述河流之间超越的依赖性，我们考虑了一个条件独立的结构。在这个模型中，河流之间的依赖关系是通过模拟一条河流的边际过剩作为其他河流的线性函数的结果来捕捉的。结果表明，一条河流的过度行为与其他河流的过度行为之间存在强烈的正相关关系。