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.
|MATLAB MOMENT-CONSTRAINED MAXIMUM ENTROPY FUNCTION FILE|
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:
Please send any your questions by emailing us.
Monash University Scholar
|Dr Kuang Ye Chow
Associate Head of School (Research Training)
|Dr Melanie Po-Leen Ooi
|Prof Serge Demidenko