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Two-component mixture models have proved to be a powerful tool for modelingheterogeneity in several cluster analysis contexts. However, most methods basedon these models assume a constant behavior for the mixture weights, which canbe restrictive and unsuitable for some applications. In this paper, we relaxthis 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 componentparameters and dynamic weights from their posterior samples. We evaluate themethod's performance by running Monte Carlo simulation studies under differentscenarios for the dynamic weights. In addition, we apply the algorithm to atime series that records the level reached by a river in southern Brazil. TheTaquari River is a water body whose frequent flood inundations have causedvarious damage to riverside communities. Implementing a dynamic mixture modelallows us to properly describe the flood regimes for the areas most affected bythese phenomena.

Identifying regime switches through Bayesian wavelet estimation: evidence from flood detection in the Taquari River Valley