  A wide range of Bayesian models have been proposed for data that is divided
hierarchically into groups. These models aim to cluster the data at different
levels of grouping, by assigning a mixture component to each datapoint, and a
mixture distribution to each group. Multi-level clustering is facilitated by
the sharing of these components and distributions by the groups. In this paper,
we introduce the concept of Degree of Sharing (DoS) for the mixture components
and distributions, with an aim to analyze and classify various existing models.
Next we introduce a generalized hierarchical Bayesian model, of which the
existing models can be shown to be special cases. Unlike most of these models,
our model takes into account the sequential nature of the data, and various
other temporal structures at different levels while assigning mixture
components and distributions. We show one specialization of this model aimed at
hierarchical segmentation of news transcripts, and present a Gibbs Sampling
based inference algorithm for it. We also show experimentally that the proposed
model outperforms existing models for the same task.
