Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Bayesian parameter estimation via variational methods
Statistics and Computing
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Expectation propagation for approximate inference in dynamic bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
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A new variant of the dynamic hierarchical model (DHM) that describes a large number of parallel time series is presented. The separate series, which may be interdependent, are modeled through dynamic linear models (DLMs). This interdependence is included in the model through the definition of a 'top-level' or 'average' DLM. The model features explicit dependences between the latent states of the parallel DLMs and the states of the average model, and thus the many parallel time series are linked to each other. The combination of dependences within each time series and dependences between the different DLMs makes the computation time that is required for exact inference cubic in the number of parallel time series, however, which is unacceptable for practical tasks that involve large numbers of parallel time series. Therefore, two methods for fast, approximate inference are proposed: a variational approximation and a factorial approach. Under these approximations, inference can be performed in linear time, and it still features exact means. Learning is implemented through a maximum likelihood (ML) estimation of the model parameters. This estimation is realized through an expectation maximization (EM) algorithm with approximate inference in the E-step. Examples of learning and forecasting on two data sets show that the addition of direct dependences has a 'smoothing' effect on the evolution of the states of the individual time series, and leads to better prediction results. The use of approximate instead of exact inference is further shown not to lead to inferior results on either data set.