Factor graphs and message passing algorithms

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Complex modern day systems are often characterized by the presence of many interacting variables that govern the dynamics of the system. Statistical inference in such systems requires efficient algorithms that offer ease of implementation while delivering the prespecified performance guarantees. In developing an algorithm for a sophisticated system, accurate and representative modeling of the underlying system is often the first step. The use of graphical models to explain the working of complex systems has gained a lot of attention in recent years. Stochastic models are often represented by a Bayesian network or a Markov random field. The graphical representation not only provides a better understanding of the system model but also offers numerous exciting opportunities to develop new and improved algorithms. Factor graphs belong to the class of graphical models that serve to explain the dependencies between several interacting variables. They can be used to model a wide variety of systems and are increasingly applied in statistical learning, signal processing, and artificial intelligence.

Original languageEnglish
Title of host publicationMathematical Foundations for Signal Processing, Communications, and Networking
PublisherCRC Press
Pages443-486
Number of pages44
ISBN (Electronic)9781439855140
ISBN (Print)9781138072169
DOIs
Publication statusPublished - 1 Jan 2017

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ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)
  • Engineering(all)

Cite this

Ahmad, A., Serpedin, E., & Qaraqe, K. (2017). Factor graphs and message passing algorithms. In Mathematical Foundations for Signal Processing, Communications, and Networking (pp. 443-486). CRC Press. https://doi.org/10.1201/9781351105668