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Fisher statistics provides an information measure which, likethat of Shannon , is a functional of a distribution f. Thelatter solves an Euler equation to minimize the functional. Tomake comparison ...
first show how Jaynes ’ Maximum Entropy Principle allows us, in the general case, to express the Fisher information content of data sets in terms of the curvature of the Shannon entropy surface ...
Standard concepts of information theory, including Shannon's entropy , Fisher's information , Jaynes ' principle of entropy maximization, Fisher's locality information matrix, and Kullback and Leibler's ...
it comparable between different samples. The clonality is a measure of oligoclonality of the sample and is calculated from Shannon's Entropy , which has been widely accepted (4). The clonality is defined ...
the amount of Shannon information [20], while our models are becoming increasingly large by leveraging powerful computers, such as the deep networks with billions of parameters. It implies that our models ...
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