@inproceedings{3ba7cfb01e7c409f99321bedd14b3fef,
title = "Nonparametric learning for Hidden Markov Models with preferential attachment dynamics",
abstract = "We address the learning problem for infinite state Hidden Markov Models (HMMs) with preferential attachment dynamics. Preferential attachment describes a 'rich get richer' process causing the HMM self transition probabilities to be proportional to the number of previous self transitions. Furthermore, the length of stay of the process in a particular state follows the Yule-Simon distribution. In describing the generative model of the hidden state processes, we use non-parametric models. We also establish the relationship of the proposed model with the Polya urn scheme and the Chinese restaurant process. The class of HMMs from this paper are applicable to data sets where the time spent in each state follows a power law. Our objective is to estimate the state sequence and the model parameters of the HMM. To that end, we propose a Gibbs sampling procedure. We evaluate the proposed procedure through computer simulations.",
keywords = "Chinese Restaurant process, Gibbs sampling, Polya urn, power law, Yule-Simon distribution",
author = "Hensley, \{Asher A.\} and Djuric, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952878",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3854--3858",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}