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Maximum Entropy Approach for Reconstructing Bivariate Probability Distributions
Published: 03 November 2014 by MDPI in 1st International Electronic Conference on Entropy and Its Applications session Information Theory
Abstract: The most considerable purpose for this study is to provide a useful algorithm combined of Maximum Entropy Method (MEM) and a computational method to predict the unique form of bivariate probability distributions. The new algorithm provides reasonable estimations for target distributions which have maximum entropy. The MEM is a powerful implement for reconstructing distribution from many types of data. In this study, we introduce this technique to estimate the important bivariate distributions which are very effective in industrial and engineering fields especially in Cybernetics and internet systems. To examine the effectiveness of our algorithm, some different simulation studies were conducted. This method will provide you the unique solution to find a probability distribution based on given information. Possessing the simple and accurate mathematical formulation and using presence-only data, MEM has become a well-suited method for different kinds of distribution modeling.
Keywords: Maximum entropy method; bivariate distribution; Shannon Entropy; computational algorithm; modeling; cybernetics