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An Introduction to Bayesian Techniques for Sensor Networks

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Wireless Algorithms, Systems, and Applications (WASA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6221))

Abstract

The purpose of this paper is threefold. First, it briefly introduces basic Bayesian techniques with emphasis on present applications in sensor networks. Second, it reviews modern Bayesian simulation methods, thereby providing an introduction to the main building blocks of the advanced Markov chain Monte Carlo and Sequential Monte Carlo methods. Lastly, it discusses new interesting research horizons.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, U.S.

    Bin Liu

Authors
  1. Bin Liu
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Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore

    Gopal Pandurangan

  2. Virginia Tech, Blacksburg, VA, USA

    V. S. Anil Kumar

  3. School of Software, Tsinghua University, 100084, Beijing, China

    Gu Ming

  4. Dept. of Computer Science, Hong Kong Univ. of Science and Technology, Kowloon, Hong Kong

    Yunhao Liu

  5. Department of Computer Science, Georgia State University, 34 Peachtree Street, Suite 1413, 30303, Atlanta, GA, USA

    Yingshu Li

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© 2010 Springer-Verlag Berlin Heidelberg

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Liu, B. (2010). An Introduction to Bayesian Techniques for Sensor Networks. In: Pandurangan, G., Anil Kumar, V.S., Ming, G., Liu, Y., Li, Y. (eds) Wireless Algorithms, Systems, and Applications. WASA 2010. Lecture Notes in Computer Science, vol 6221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14654-1_40

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  • DOI: https://doi.org/10.1007/978-3-642-14654-1_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14653-4

  • Online ISBN: 978-3-642-14654-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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