A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields

This paper presents recursive cavity modeling - a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to subdivide the random field into smaller subfields, constructing cavity models which approximate these subfields. Each cavi...

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Veröffentlicht in:	IEEE transactions on image processing 2008-01, Vol.17 (1), p.70-83
Hauptverfasser:	Johnson, J.K., Willsky, A.S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Approximate inference Approximation Artificial Intelligence Blanketing Computer Simulation Construction Exact sciences and technology Gaussian approximation Gaussian Markov random fields Graphical models Holes Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Inference Inference algorithms information projection Information, signal and communications theory Iterative methods Markov Chains Markov random fields Mathematical analysis Mathematical models maximum entropy model reduction Models, Statistical Normal Distribution Pattern Recognition, Automated - methods Physics Recursive Recursive estimation Remote sensing Reproducibility of Results Sensitivity and Specificity Sensor phenomena and characterization Signal processing Statistics Studies Telecommunications and information theory
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