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A novel hierarchical Bayesian HMM for multi-dimensional discrete data. (English) Zbl 1157.68429
Gammerman, A. (ed.), Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press (ISBN 978-0-88986-710-9/CD-ROM). 52-57 (2008).
Summary: This paper proposes a novel Bayesian Hidden Markov Model for multi-dimensional discrete time-series data. The proposed model has hyperparameters, which correspond to the dependencies of the data components on the hidden states. By adjusting these hyperparameters, the proposed model enables a reduction in negative influences from ineffective data components. This paper also describes an implementation method for the proposed model using the Markov Chain Monte Carlo method. The performance of the proposed model is evaluated via two examples.
For the entire collection see [Zbl 1154.68012].
MSC:
68T05 Learning and adaptive systems in artificial intelligence
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