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JMLR
2010
137views more  JMLR 2010»
14 years 8 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
JMLR
2000
134views more  JMLR 2000»
15 years 1 months ago
Learning with Mixtures of Trees
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu...
Marina Meila, Michael I. Jordan
BMCBI
2006
239views more  BMCBI 2006»
15 years 1 months ago
Applying dynamic Bayesian networks to perturbed gene expression data
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics,...
Norbert Dojer, Anna Gambin, Andrzej Mizera, Bartek...
IJCAI
2007
15 years 3 months ago
A Bayesian Computational Cognitive Model
Computational cognitive modeling has recently emerged as one of the hottest issues in the AI area. Both symbolic approaches and connectionist approaches present their merits and d...
Zhiwei Shi, Hong Hu, Zhongzhi Shi
ICCV
2007
IEEE
16 years 3 months ago
Graph Based Discriminative Learning for Robust and Efficient Object Tracking
Object tracking is viewed as a two-class 'one-versusrest' classification problem, in which the sample distribution of the target is approximately Gaussian while the back...
Xiaoqin Zhang, Weiming Hu, Stephen J. Maybank, Xi ...