Jagat Narain Kapur (Author of Maximum-Entropy Models in Science and Engineering)
Maximum Entropy Tutorial: Intro To Max Ent
Maximum-Entropy Models in Science and Engineering.
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The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge is the one with largest entropy , in the context of precisely stated prior data such as a proposition that expresses testable information. Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function. Consider the set of all trial probability distributions that would encode the prior data. According to this principle, the distribution with maximal information entropy is the best choice. The principle was first expounded by E. Jaynes in two papers in   where he emphasized a natural correspondence between statistical mechanics and information theory.
A state-of-the-art description of the theory and applications of the various entropy optimization principles is given. The relation between information-theoretic entropy and thermodynamic entropy is specially recalled in the context of the more general relationship that exist between what are designated as primary and secondary entropies. Unable to display preview. Download preview PDF. Skip to main content. Advertisement Hide.