Advanced inference in Bayesian networks
Learning in graphical models
A unifying review of linear Gaussian models
Neural Computation
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Machine Learning
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Relational object maps for mobile robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
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Kalman Filtering is a computational tool with widespread applications in robotics, financial and weather forecasting, environmental engineering and defense. Given observation and state transition models, the Kalman Filter (KF) recursively estimates the state variables of a dynamic system. However, the KF requires a cubic time matrix inversion operation at every timestep which prevents its application in domains with large numbers of state variables. We propose Relational Gaussian Models to represent and model dynamic systems with large numbers of variables efficiently. Furthermore, we devise an exact lifted Kalman Filtering algorithm which takes only linear time in the number of random variables at every timestep. We prove that our algorithm takes linear time in the number of state variables even when individual observations apply to each variable. To our knowledge, this is the first lifted (linear time) algorithm for filtering with continuous dynamic relational models.