Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model
نویسندگان
چکیده
The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical for learning from sequential and time-series data. A sticky HDP-HMM proposed to strengthen self-persistence probability in HDP-HMM. However, entangles strength prior transition together, limiting its expressiveness. Here, we propose more general model: disentangled (DS-HDP-HMM). We develop novel Gibbs sampling algorithms efficient inference this model. show that outperforms on both synthetic real data, apply new approach analyze neural data segment behavioral video
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2021
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-030-67658-2_35