An introduction to hidden Markov models and Bayesian networks
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Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches
Neural Processing Letters
Modular neural architectures for robotics
Biologically inspired robot behavior engineering
Machine Learning for Computer Graphics: A Manifesto and Tutorial
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Hierarchical models of variance sources
Signal Processing - Special issue on independent components analysis and beyond
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models
Neural Processing Letters
Nested Monte Carlo EM Algorithm for Switching State-Space Models
IEEE Transactions on Knowledge and Data Engineering
Learning discontinuities with products-of-sigmoids for switching between local models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Combining discriminative features to infer complex trajectories
ICML '06 Proceedings of the 23rd international conference on Machine learning
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
The Journal of Machine Learning Research
Mixed-state models for nonstationary multiobject activities
EURASIP Journal on Applied Signal Processing
A Bayesian network approach to explaining time series with changing structure
Intelligent Data Analysis
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
Particle filters for real-time fault diagnosis in hybrid systems
ROCOM'07 Proceedings of the 7th WSEAS International Conference on Robotics, Control & Manufacturing Technology
Contour graph based human tracking and action sequence recognition
Pattern Recognition
Improving the recognition of interleaved activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Known Unknowns: Novelty Detection in Condition Monitoring
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Online annotation and prediction for regime switching data streams
Proceedings of the 2009 ACM symposium on Applied Computing
A dynamic mixture model to detect student motivation and proficiency
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Data-driven MCMC for learning and inference in switching linear dynamic systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Switching Hidden Markov Models for Learning of Motion Patterns in Videos
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
GIMscan: a new statistical method for analyzing whole-genome array CGH data
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Modeling music as a dynamic texture
IEEE Transactions on Audio, Speech, and Language Processing
Approximate forward-backward algorithm for a switching linear Gaussian model
Computational Statistics & Data Analysis
eMOSAIC model for humanoid robot control
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Planning in partially-observable switching-mode continuous domains
Annals of Mathematics and Artificial Intelligence
Variational mixture smoothing for non-linear dynamical systems
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Detection of hidden structures in nonstationary spike trains
Neural Computation
Automating the calibration of a neonatal condition monitoring system
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Kalman filter-based facial emotional expression recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Expectation propagation for approximate inference in dynamic bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Dynamic trees: a structured variational method giving efficient propagation rules
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Inference in hybrid networks: theoretical limits and practical algorithms
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Binding statistical and machine learning models for short-term forecasting of global solar radiation
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
The eMOSAIC model for humanoid robot control
Neural Networks
Aggregating web offers to determine product prices
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Review: A review of novelty detection
Signal Processing
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We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models— hidden Markov models and linear dynamical systems—and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.