  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.
