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arXiv · 1706.06178

Infinite Mixture Model of Markov Chains

Jan Reubold·Thorsten Strufe·Ulf Brefeld
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Abstract

We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme. It models categorical-valued time series that exhibit dynamics from multiple underlying patterns (e.g. user behavior traces). We simplify the idea of capturing these patterns by hierarchical hidden Markov models (HHMMs) - and extend the existing approaches by the additional representation of structural information. Our empirical results are based on both synthetic- and real world data. They indicate that the results are easily interpretable, and that the model excels at segmentation and prediction performance: it successfully identifies the generating patterns and can be used for effective prediction of future observations.

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Jan Reubold, Thorsten Strufe, Ulf Brefeld. 2017-06-19. Infinite Mixture Model of Markov Chains. https://arxiv.org/abs/1706.06178

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