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NIPS
2008
14 years 11 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
ECCV
2006
Springer
15 years 12 months ago
Robust Multi-body Motion Tracking Using Commute Time Clustering
Abstract. The presence of noise renders the classical factorization method almost impractical for real-world multi-body motion tracking problems. The main problem stems from the ef...
Huaijun Qiu, Edwin R. Hancock
SIGMOD
2005
ACM
151views Database» more  SIGMOD 2005»
15 years 10 months ago
Immortal DB: transaction time support for SQL server
Immortal DB builds transaction time database support into the SQL Server engine, not in middleware. Transaction time databases retain and provide access to prior states of a datab...
David B. Lomet, Roger S. Barga, Mohamed F. Mokbel,...
ICDE
2006
IEEE
133views Database» more  ICDE 2006»
15 years 4 months ago
Transaction Time Support Inside a Database Engine
Transaction time databases retain and provide access to prior states of a database. An update “inserts” a new record while preserving the old version. Immortal DB builds trans...
David B. Lomet, Roger S. Barga, Mohamed F. Mokbel,...
JMLR
2010
137views more  JMLR 2010»
14 years 4 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton