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» Temporal causal modeling with graphical granger methods
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KDD
2009
ACM
172views Data Mining» more  KDD 2009»
14 years 1 days ago
Learning dynamic temporal graphs for oil-production equipment monitoring system
Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learn...
Yan Liu, Jayant R. Kalagnanam, Oivind Johnsen
AI
1998
Springer
13 years 7 months ago
Reasoning About Actions: Steady Versus Stabilizing State Constraints
In formal approaches to commonsense reasoning about actions. the Ramification Problem denotes the problem of handling indirect effects which implicitly derive from so-called state...
Michael Thielscher
ICPR
2004
IEEE
14 years 8 months ago
BTF Image Space Utmost Compression and Modelling Method
The bidirectional texture function (BTF) describes texture appearance variations due to varying illumination and viewing conditions. This function is acquired by large number of m...
Jirí Filip, Michael Arnold, Michal Haindl
UAI
2008
13 years 9 months ago
Efficient Inference in Persistent Dynamic Bayesian Networks
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interve...
Tomás Singliar, Denver Dash
ICCV
2003
IEEE
14 years 22 days ago
Markov-Based Failure Prediction for Human Motion Analysis
This paper presents a new method of detecting and predicting motion tracking failures with applications in human motion and gait analysis. We define a tracking failure as an event...
Shiloh L. Dockstader, Nikita S. Imennov, A. Murat ...