Abstract
In sequentially observed data, Bayesian partition models aim at par- titioning the entire observation period into disjoint clusters. Each cluster is an aggregation of sequential observations and a simple model is adopted within each cluster. The main inferential problem is the estimation of the number and loca- tions of the clusters. We extend the well-known product partition model (PPM) by assuming that observations within the same cluster have their distributions in- dexed by correlated and difierent parameters. Such parameters are similar within a cluster by means of a Gibbs prior distribution. We carried out several simula- tions and real data set analyses showing that our model provides better estimates for all parameters, including the number and position of the temporal clusters, even for situations favoring the PPM. A free and open source code is available.
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