Two experimental designs are compared, a traditional factorial design and a maximum entropy design.
Interestingly, the maximum entropy design performed comparably with the random approach in this dataset.
To study the effect of the number of available models on the performance of the maximum entropy design, we fixed the number of unknown species to 16 while increasing the number of available models over 10, 30, 50, 70, 100 and up to 200.
In this paper, we apply the maximum entropy test designed for a goodness of fit in iid samples (cf. Lee et al. (2011)) to GARCH 1,1) models.
To address the special aspects of this classification challenge, specifically along the Focus and the Evidence dimensions, a new classification model based on Maximum Entropy was designed, and experimented with, in addition to standard classifiers.
As a gof test based on this result, the maximum entropy type test designed by Lee et al.
Unlike the random forest, the maximum entropy model is designed to work with sub-optimum sample size as it predicts the distribution when it is under-sampled as in most signal processing problems [38].
The maximum entropy test, as designed for examining goodness-of-fit with a non-robust estimator such as the maximum likelihood estimator, can suffer from severe size distortions when the data are contaminated by outliers.
Several designs are close to having maximum entropy, which means that the designs are robust.
This paper presents an efficient method for solving the economic dispatch problem (EDP) through combination of genetic algorithm (GA), the sequential quadratic programming (SQP) technique, uniform design technique, the maximum entropy principle, simplex crossover and non-uniform mutation.
