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JMLR
2006
92views more  JMLR 2006»
13 years 7 months ago
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
We consider an optimization problem in probabilistic inference: Given n hypotheses Hj, m possible observations Ok, their conditional probabilities pk j, and a particular Ok, selec...
Anders Bergkvist, Peter Damaschke, Marcel Lüt...
ICDM
2010
IEEE
150views Data Mining» more  ICDM 2010»
13 years 5 months ago
Probabilistic Inference Protection on Anonymized Data
Background knowledge is an important factor in privacy preserving data publishing. Probabilistic distributionbased background knowledge is a powerful kind of background knowledge w...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Ke Wang, Y...
IJAR
2006
80views more  IJAR 2006»
13 years 7 months ago
Operations for inference in continuous Bayesian networks with linear deterministic variables
An important class of continuous Bayesian networks are those that have linear conditionally deterministic variables (a variable that is a linear deterministic function of its pare...
Barry R. Cobb, Prakash P. Shenoy
ALIFE
2008
13 years 7 months ago
Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data
The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and ...
Koenraad Van Leemput, Tim Van den Bulcke, Thomas D...
SCESM
2006
ACM
269views Algorithms» more  SCESM 2006»
14 years 1 months ago
Inferring operational requirements from scenarios and goal models using inductive learning
Goal orientation is an increasingly recognised Requirements Engineering paradigm. However, integration of goal modelling with operational models remains an open area for which the...
Dalal Alrajeh, Alessandra Russo, Sebastián ...