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：双组分混合模型已被证明是在多种聚类分析背景下对异质性建模的强大工具。然而，基于此类模型的大多数方法假设混合权重保持恒定，这在某些应用中可能具有局限性且不适用。本文放宽了这一假设，允许混合权重随指标（如时间）变化，使模型能够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，从其后验样本中联合估计组分参数与动态权重。通过在不同动态权重场景下进行蒙特卡洛模拟研究，评估了该方法的性能。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数据。塔夸里河是一条水体，其频繁的洪水泛滥对沿岸社区造成了多重损害。实施动态混合模型使我们能够正确描述受这些现象影响最严重区域的洪水体制。