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arXiv · 2004.03019

Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model

Ding Zhou·Yuanjun Gao·Liam Paninski
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Abstract

The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical Hidden Markov Model for learning from sequential and time-series data. A sticky extension of the HDP-HMM has been proposed to strengthen the self-persistence probability in the HDP-HMM. However, the sticky HDP-HMM entangles the strength of the self-persistence prior and transition prior together, limiting its expressiveness. Here, we propose a more general model: the disentangled sticky HDP-HMM (DS-HDP-HMM). We develop novel Gibbs sampling algorithms for efficient inference in this model. We show that the disentangled sticky HDP-HMM outperforms the sticky HDP-HMM and HDP-HMM on both synthetic and real data, and apply the new approach to analyze neural data and segment behavioral video data.

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Ding Zhou, Yuanjun Gao, Liam Paninski. 2020-06-22. Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model. https://arxiv.org/abs/2004.03019

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