Beam sampling for the infinite hidden Markov model 论文
2008引用 224
Bayesian Methods and Mixture ModelsAlgorithms and Data CompressionMachine Learning and Algorithms
详细信息
- 发表日期
- 2008-01-01
- 发表年份
- 2008
关键词
Bayesian Methods and Mixture ModelsAlgorithms and Data CompressionMachine Learning and Algorithms
摘要
The infinite hidden Markov model is a non-parametric 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.