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Adaptive Applications of Maximum Entropy Principle

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1198))

Abstract

The probability distribution of a system can be adaptively derived using the maximum entropy principle subject to its information set in terms of probabilistic moments. The obtained probability  distribution characterizes the wide range of exponential family of distributions when one maximizes Shannon entropy. On maximizing Tsallis entropy with non-extensive parameter q, power law distributions are obtained which portrays the well-known Shannon family of exponential distribution as, \(q \to 1\). The maximization of Shannon entropy subject to the shifted geometric mean constraints leads to a probability distribution in terms of Hurwitz zeta function. This density characterizes the equilibrium state of broadband network traffic. Moreover, maximization of Shannon entropy in Laplace domain subject to specific constraints provides a transient probability distribution which characterizes the behavior of M/M/1/1 queueing system.

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Author information

Authors and Affiliations

  1. Jawaharlal Nehru University, New Delhi, India

    Amit Kumar Singh & Nikhil Kumar Rajput

  2. Ravenshaw University, Cuttack, Odisha, 753003, India

    Dilip Senapati & Tanmay Mukherjee

Authors
  1. Amit Kumar Singh
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  2. Dilip Senapati
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  3. Tanmay Mukherjee
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  4. Nikhil Kumar Rajput
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Corresponding author

Correspondence to Amit Kumar Singh .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  3. Department of Computer Science, University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Singh, A.K., Senapati, D., Mukherjee, T., Rajput, N.K. (2021). Adaptive Applications of Maximum Entropy Principle. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-6584-7_36

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  • DOI: https://doi.org/10.1007/978-981-15-6584-7_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6583-0

  • Online ISBN: 978-981-15-6584-7

  • eBook Packages: EngineeringEngineering (R0)

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