Two-component mixture models have proved to be a powerful tool for modeling heterogeneity in several cluster analysis contexts. However, most methods based on these models assume a constant behavior for the mixture weights, which can be restrictive and unsuitable for some applications. In this paper, we relax this assumption and allow the mixture weights to vary according to the index (e.g., time) to make the model more adaptive to a broader range of data sets. We propose an efficient MCMC algorithm to jointly estimate both component parameters and dynamic weights from their posterior samples. We evaluate the method's performance by running Monte Carlo simulation studies under different scenarios for the dynamic weights. In addition, we apply the algorithm to a time series that records the level reached by a river in southern Brazil. The Taquari River is a water body whose frequent flood inundations have caused various damage to riverside communities. Implementing a dynamic mixture model allows us to properly describe the flood regimes for the areas most affected by these phenomena.

翻译：双组分混合模型已在多个聚类分析场景中被证明是建模异质性的有力工具。然而，基于此类模型的大多数方法假设混合权重具有恒定行为，这在某些应用中可能具有限制性且不适用。本文放松了这一假设，允许混合权重根据索引（例如时间）变化，从而使模型能更灵活地适应更广泛的数据集。我们提出了一种高效的MCMC算法，通过后验样本联合估计组分参数与动态权重。我们通过在不同动态权重情景下运行蒙特卡洛模拟研究来评估该方法的性能。此外，我们将该算法应用于记录巴西南部某河流水位的时间序列。塔夸里河是一条水体，其频繁的洪水泛滥已对沿岸社区造成多种损害。实施动态混合模型使我们能够恰当地描述受这些现象影响最严重地区的洪水机制。