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» Convolutional Factor Graphs as Probabilistic Models
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JSAC
1998
126views more  JSAC 1998»
13 years 5 months ago
Iterative Decoding of Compound Codes by Probability Propagation in Graphical Models
Abstract—We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms. After reviewing a variety of graphical models (...
Frank R. Kschischang, Brendan J. Frey
RECOMB
2010
Springer
14 years 20 days ago
Incremental Signaling Pathway Modeling by Data Integration
Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology. Pathway model construction is often an inherently incremental...
Geoffrey Koh, David Hsu, P. S. Thiagarajan
IAT
2007
IEEE
14 years 3 days ago
Analysis of Multi-Actor Policy Contexts Using Perception Graphs
Policy making is a multi-actor process: it involves a variety of actors, each trying to further their own interests. How these actors decide and act largely depends on the way the...
Pieter W. G. Bots
JCB
2006
185views more  JCB 2006»
13 years 5 months ago
A Probabilistic Methodology for Integrating Knowledge and Experiments on Biological Networks
Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed ...
Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...
ICIP
2005
IEEE
14 years 7 months ago
Variable module graphs: a framework for inference and learning in modular vision systems
We present a novel and intuitive framework for building modular vision systems for complex tasks such as surveillance applications. Inspired by graphical models, especially factor...
Amit Sethi, Mandar Rahurkar, Thomas S. Huang