document: 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 .
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