×

Graphical models. (English) Zbl 1057.62001

Summary: Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. We review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to be deployed in large-scale data analysis problems. We also present examples of graphical models in bioinformatics, error-control coding and language processing.

MSC:

62A09 Graphical methods in statistics
62P10 Applications of statistics to biology and medical sciences; meta analysis
05C90 Applications of graph theory
92D10 Genetics and epigenetics
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Chong C Y, Castanon G, Cooprider N, et al. Efficient multiple hypothesis tracking by track segment graph. In: 12th International Conference on Information Fusion, Seattle, 2009. 2177-2184
[2] Jordan M I. Graphical models. Stat Sci, 2004, 19: 140-155 · Zbl 1057.62001
[3] Kschischang F R, Frey B J, Loeliger H. Factor graphs and the sum-product algorithm. IEEE Trans Inf Theory, 2001, 47: 498-519 · Zbl 0998.68234
[4] Panakkal V P, Velmurugan R. Effective data association scheme for tracking closely moving targets using factor graphs. In: 17th National Conference on Communications, Bangalore, 2011. 1-5
[5] Xu J, Li J, Xu S. Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering. Sci China Inf Sci, 2012, 55: 530-544 · Zbl 1245.93133
[6] Lu S, Ma Y, Yang W. An effective data fusion and track prediction approach for multiple sensors. In: International Conference on Computational Intelligence and Software Engineering, Wuhan, 2010. 1-4
[7] Cox I J, Hingorani S. An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans Patt Anal Mach Intell, 1996, 18: 138-150
[8] Murty K G. An algorithm for ranking all the assignments in order of increasing cost. Oper Res, 1968, 16: 682-687 · Zbl 0214.18705
[9] Reid D B. An algorithm for tracking multiple targets. IEEE Trans Automat Contr,
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.