Uncertainty in Artificial Intelligence
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Products of Hidden Markov Models: It Takes N>1 to Tango
Graham Taylor, Geoffrey Hinton
Abstract:
Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to their more computationally expensive gradient-based learning algorithm,and the intractability of computing the log likelihood of sequences under the model. In this paper, we demonstrate how the partition function can be estimated reliably via Annealed Importance Sampling. We perform experiments using contrastive divergence learning on rainfall data and data captured from pairs of people dancing. Our results suggest that advances in learning and evaluation for undirected graphical models and recent increases in available computing power make PoHMMs worth considering for complex time-series modeling tasks.
Keywords: null
Pages: 522-529
PS Link:
PDF Link: /papers/09/p522-taylor.pdf
BibTex:
@INPROCEEDINGS{Taylor09,
AUTHOR = "Graham Taylor and Geoffrey Hinton",
TITLE = "Products of Hidden Markov Models: It Takes N>1 to Tango",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "522--529"
}


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