A Generative Probabilistic Approach to Visualizing Sets of Symbolic Sequences

There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types. In this paper we develop a generative probabilistic model for visualizing sets of discrete symbolic sequences. The model, a constrained mixture of discrete hidden Markov models, is a generalization of density-based visualization methods previously developed for static data sets. We illustrate our approach on sequences representing web-log data and chorals by J.S. Bach