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2008
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Beam sampling for the infinite hidden Markov model

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Beam sampling for the infinite hidden Markov model
The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm for the infinite Hidden Markov model called beam sampling. Beam sampling combines slice sampling, which limits the number of states considered at each time step to a finite number, with dynamic programming, which samples whole state trajectories efficiently. Our algorithm typically outperforms the Gibbs sampler and is more robust. We present applications of iHMM inference using the beam sampler on changepoint detection and text prediction problems.
Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubi
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubin Ghahramani
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