The probability distribution is often sought in engineering for the purpose of expanded
uncertainty evaluation and reliability analysis. Although there are various methods available to approximate
the distribution, one of the commonly used ones is the method based on statistical moments (or cumulants).
Given these parameters, the corresponding solution can be reliably approximated using various algorithms.
However, the commonly used algorithms are limited by only four moments and assume that the corresponding
distribution is unimodal. Therefore, this paper analyzes the performance of a relatively new and an
improved parametric distribution tting technique known as the moment-constrained maximum entropy
method, which overcomes these shortcomings. It is shown that the uncertainty (or reliability) estimation
quality of the proposed method improves with the number of moments regardless of the distribution modality.
Finally, the paper uses case studies from a lighting retro t project and an electromagnetic sensor design
problem to substantiate the computational ef ciency and numerical stability of the moment method in
design optimization problems. The results and discussions presented in the paper could guide engineers
in employing the maximum entropy method in a manner that best suits their respective systems.