Tracking and data association
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Connectionist learning of belief networks
Artificial Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
Neural Computation
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
An introduction to variational methods for graphical models
Learning in graphical models
Using Learning for Approximation in Stochastic Processes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Dynamic bayesian networks for information fusion with applications to human-computer interfaces
Dynamic bayesian networks for information fusion with applications to human-computer interfaces
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A dynamic Bayesian approach to computational Laban shape quality analysis
Advances in Human-Computer Interaction
A graphical model framework for coupling MRFs and deformable models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Many real-valued stochastic time-series are locally linear (Gaussian), but globally nonlinear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hidden Markov model drives a linear dynamic system. This combination allows us to model both the discrete and continuous causes of trajectories such as human gestures. The number of computations needed for exact inference is exponential in the sequence length, so we derive an approximate variational inference technique that can also be used to learn the parameters of the discrete and continuous models. We show how the mixed-state model and the variational technique can be used to classify human hand gestures made with a computer mouse.