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Moment-constrained Maximum Entropy Method

The principle of maximum entropy (MaxEnt) overcomes the limitations of the commonly used moments-based distribution fitting techniques. The effectiveness of the algorithm has been studied for the evaluation of expanded uncertainty, and shown to be superior to the other distribution fitting techniques, such as the Pearson and Johnson systems of distribution. This can be observed from the plots below. The moment-contrained MaxEnt is also a Matlab toolbox and it has been developed for a more stable performance by Arvind Rajan, Melanie Po-Leen Ooi, Ye Chow Kuang, and Serge Demidenko in year 2017. The moment-constrained MaxEnt algorithm consists of a series of other MATLAB functions, such as minFunc and Legendre-Gauss quadrature weights and nodes. The MATLAB function of the Maxent algorithm can be obtained by clicking on the button below.

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Minor part of the results from the MaxEnt algorithm has been published in the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) in Torino, Italy. Hence the best citation for using the MaxEnt algorithm function is:

  • A. Rajan, Y. C. Kuang, M. P.-L. Ooi, and S. Demidenko, "Moments and maximum entropy method for expanded uncertainty estimation in measurements," 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017

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  • Monash University
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  • IEEE Instrumentation and Measurement Society, Technical Committee 32 - Fault Tolerance Measurement Systems

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    Arvind Rajan

    Monash University Scholar
    Electrical and Computer Systems Engineering


    Dr Kuang Ye Chow

    Associate Head of School (Research Training)
    Electrical and Computer Systems Engineering
    Monash University

    Dr Melanie Po-Leen Ooi

    Associate Professor
    Electrical, Electronic & Computer Engineering
    Heriot-Watt University

    Prof Serge Demidenko

    Associate Head
    School of Engineering & Advanced Technology
    Massey University