Beam Sampling for the Infinite Hidden Markov Model
Jurgen van Gael, Yunus Saatci, Yee Whye Teh and Zoubin Ghahramani
International Conference on Machine Learning
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 nite 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.