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JMLR
2010
137views more  JMLR 2010»
13 years 3 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
TAMC
2010
Springer
13 years 7 months ago
Exploiting Restricted Linear Structure to Cope with the Hardness of Clique-Width
Clique-width is an important graph parameter whose computation is NP-hard. In fact we do not know of any other algorithm than brute force for the exact computation of clique-width...
Pinar Heggernes, Daniel Meister, Udi Rotics
GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
14 years 6 days ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
ML
2006
ACM
142views Machine Learning» more  ML 2006»
13 years 8 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
UAI
2008
13 years 10 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