![]() We anticipate our principle finding broadĪpplications in diverse fields due to generalizing the traditional maximumĮntropy method with the ability to utilize uncertain observations. Previous remedies either relaxed featureĬonstraints when accounting for observation error, given well-characterizedĮrrors such as zero-mean Gaussian, or chose to simply select the most likely Output of a black-box machine learning model as input into an uncertain maximumĮntropy model, resulting in a novel approach for scenarios where the Surpassing the accuracy of some ad-hoc methods. Show the Principle of Uncertain Maximum Entropy as a method that both encodesĪll available information in spite of arbitrarily noisy observations while After de ning entropy and computing it in some examples, we will describe this principle and see how it provides a natural conceptual role for many standard probability distributions (normal, exponential, Laplace, Bernoulli). Occlusion is present, possibilities arise for which standard maximum entropyĪpproaches may fail, as they are unable to match feature constraints. It is called the principle of maximum entropy. The model elements are not directly observable, such as when noise or ocular ![]() However, when we consider situations in which To the acquisition of unbiased models, whilst deepening the understanding ofĬomplex systems and phenomena. Its resultant solutions have servedĪs a catalyst, facilitating researchers in mapping their empirical observations ![]() Mechanics, Machine Learning, and Ecology. Theory, has contributed to advancements in various domains such as Statistical Download a PDF of the paper titled The Principle of Uncertain Maximum Entropy, by Kenneth Bogert and 1 other authors Download PDF Abstract: The principle of maximum entropy, as introduced by Jaynes in information ![]()
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