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» Importance Sampling for Continuous Time Bayesian Networks
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KDD
2009
ACM
364views Data Mining» more  KDD 2009»
16 years 10 days ago
Causality quantification and its applications: structuring and modeling of multivariate time series
Time series prediction is an important issue in a wide range of areas. There are various real world processes whose states vary continuously, and those processes may have influenc...
Takashi Shibuya, Tatsuya Harada, Yasuo Kuniyoshi
SIES
2007
IEEE
15 years 6 months ago
Refactoring an Automotive Embedded Software Stack using the Component-Based Paradigm
Abstract— The number of electronic systems in cars is continuously growing. Electronic systems, consisting of so-called electronic control units (ECUs) interconnected by a commun...
Thomas M. Galla, Dietmar Schreiner, Wolfgang Forst...
JMLR
2010
157views more  JMLR 2010»
14 years 6 months ago
Why are DBNs sparse?
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
Shaunak Chatterjee, Stuart Russell
OSDI
2006
ACM
16 years 2 days ago
Fidelity and Yield in a Volcano Monitoring Sensor Network
We present a science-centric evaluation of a 19-day sensor network deployment at Reventador, an active volcano in Ecuador. Each of the 16 sensors continuously sampled seismic and ...
Geoffrey Werner-Allen, Konrad Lorincz, Jeff Johnso...
CONEXT
2006
ACM
15 years 5 months ago
Synergy: blending heterogeneous measurement elements for effective network monitoring
Network traffic matrices are important for various network planning and management operations. Previous work for estimation of traffic matrices is based on either link load record...
Awais Ahmed Awan, Andrew W. Moore