A model for reasoning about persistence and causation
Computational Intelligence
Hidden Markov models for speech recognition
Technometrics
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Proceedings of the 1997 conference on Advances in neural information processing systems 10
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Modeling acoustic correlations by factor analysis
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A unifying review of linear Gaussian models
Neural Computation
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A minimum description length framework for unsupervised learning
A minimum description length framework for unsupervised learning
Variational Learning for Switching State-Space Models
Neural Computation
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Categorization and Learning of Pen Motion Using Hidden Markov Models
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Hidden Markov Models with Multiple Observers
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Large-scale bot detection for search engines
Proceedings of the 19th international conference on World wide web
Bayesian fusion of hidden Markov models for understanding bimanual movements
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.