An introduction to factor graphs

by: HA Loeliger

Signal Processing Magazine, IEEE, Vol. 21, No. 1. (2004), pp. 28-41.

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DOI, IEEE Explore, http://www.robots.ox.ac.uk/~parg/mlrg/pap…

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Abstract

Graphical models such as factor graphs allow a unified approach to a number of key topics in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes and similar codes, joint decoding, equalization, parameter estimation, hidden-Markov models, Kalman filtering, and recursive least squares. Graphical models can represent complex real-world systems, and such representations help to derive practical detection/estimation algorithms in a wide area of applications. Most known signal processing techniques -including gradient methods, Kalman filtering, and particle methods -can be used as components of such algorithms. Other than most of the previous literature, we have used Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams.

@article{citeulike:2296567, abstract = {Graphical models such as factor graphs allow a unified approach to a number of key topics in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes and similar codes, joint decoding, equalization, parameter estimation, hidden-Markov models, Kalman filtering, and recursive least squares. Graphical models can represent complex real-world systems, and such representations help to derive practical detection/estimation algorithms in a wide area of applications. Most known signal processing techniques -including gradient methods, Kalman filtering, and particle methods -can be used as components of such algorithms. Other than most of the previous literature, we have used Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams.}, author = {Loeliger, H. A. }, booktitle = {Signal Processing Magazine, IEEE}, citeulike-article-id = {2296567}, doi = {10.1109/MSP.2004.1267047}, journal = {Signal Processing Magazine, IEEE}, keywords = {graph, graphical-models}, number = {1}, pages = {28--41}, posted-at = {2008-05-07 19:38:25}, priority = {2}, title = {An introduction to factor graphs}, url = {http://dx.doi.org/10.1109/MSP.2004.1267047}, volume = {21}, year = {2004} }

RIS record

TY - JOUR ID - citeulike:2296567 L3 - citeulike-article-id:2296567 TI - An introduction to factor graphs T2 - Signal Processing Magazine, IEEE JF - Signal Processing Magazine, IEEE VL - 21 IS - 1 SP - 28 EP - 41 N2 - Graphical models such as factor graphs allow a unified approach to a number of key topics in coding and signal processing such as the iterative decoding of turbo codes, LDPC codes and similar codes, joint decoding, equalization, parameter estimation, hidden-Markov models, Kalman filtering, and recursive least squares. Graphical models can represent complex real-world systems, and such representations help to derive practical detection/estimation algorithms in a wide area of applications. Most known signal processing techniques -including gradient methods, Kalman filtering, and particle methods -can be used as components of such algorithms. Other than most of the previous literature, we have used Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams. KW - graph KW - graphical-models AU - Loeliger, HA PY - 2004/// UR - http://dx.doi.org/10.1109/MSP.2004.1267047 ER -

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