Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Detecting Abnormal Events via Hierarchical Dirichlet Processes
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
The infinite HMM for unsupervised PoS tagging
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Inducing synchronous grammars with slice sampling
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Unsupervised event coreference resolution with rich linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Sticky hidden Markov modeling of comparative genomic hybridization
IEEE Transactions on Signal Processing
Multi-granularity video unusual event detection based on infinite Hidden Markov models
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Modeling the dynamics of social networks using Bayesian hierarchical blockmodels
Statistical Analysis and Data Mining
Generative Models for Evolutionary Clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Sequential minimal optimization in convex clustering repetitions
Statistical Analysis and Data Mining
Adaptive Bayesian HMM for Fully Unsupervised Chinese Part-of-Speech Induction
ACM Transactions on Asian Language Information Processing (TALIP)
A hierarchical dirichlet process model for joint part-of-speech and morphology induction
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Uncovering spatial topology represented by rat hippocampal population neuronal codes
Journal of Computational Neuroscience
Exact sampling and decoding in high-order hidden Markov models
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Bayesian nonparametric hidden semi-Markov models
The Journal of Machine Learning Research
Computer Vision and Image Understanding
An overview of bayesian methods for neural spike train analysis
Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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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.