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» Complexity of Inference in Graphical Models
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CORR
2011
Springer
191views Education» more  CORR 2011»
13 years 2 months ago
Picturing classical and quantum Bayesian inference
We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum...
Bob Coecke, Robert W. Spekkens
ICML
2010
IEEE
13 years 8 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
ICCV
2005
IEEE
14 years 9 months ago
Probabilistic Contour Extraction Using Hierarchical Shape Representation
In this paper, we address the issue of extracting contour of the object with a specific shape. A hierarchical graphical model is proposed to represent shape variations. A complex ...
Xin Fan, Chun Qi, Dequn Liang, Hua Huang
JMLR
2010
141views more  JMLR 2010»
13 years 2 months ago
FastInf: An Efficient Approximate Inference Library
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...
ICSE
2008
IEEE-ACM
14 years 8 months ago
Tool support for the navigation in graphical models
Graphical models are omnipresent in the software engineering field, but most current graphical modeling languages do not scale with the increasing size and complexity of today...
Tobias Reinhard, Silvio Meier, Reinhard Stoiber, C...