# An Infinite Hidden Markov Model With Similarity-Biased Transitions

@inproceedings{Dawson2017AnIH, title={An Infinite Hidden Markov Model With Similarity-Biased Transitions}, author={C. Dawson and Chaofan Huang and C. Morrison}, booktitle={ICML}, year={2017} }

We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states. This is accomplished by defining a similarity function on the state space and scaling transition probabilities by pair-wise similarities, thereby inducing correlations among the transition distributions. We present an augmented data representation of the model as a Markov Jump Process in which… Expand

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