Titlebook: Information Theoretic Principles for Agent Learning; Jerry D. Gibson Book 2025 The Editor(s) (if applicable) and The Author(s), under excl

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Jerry D. Gibsonmherst, and the medical and dental school to the original academic campus: and (3) the merger with the State University of New York System in 1962. Despite these significant transitions, any one of which could have changed the intrinsic integrity of UB and disrupted the bonding between community and universit978-1-4020-0367-7978-0-306-46879-7

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Differential Entropy, Entropy Rate, and Maximum Entropy,nt in its properties that it is given a new name, .. Treatments of differential entropy are standard in basic information theory texts and in any first course in information theory. So, much of the material in Sect. . is widely available. The topics of entropy rate, entropy power, and maximum entrop

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Typical Sequences and the AEP,sented. The idea of typical sequences and a typical set are fundamentals in information theoretic developments of lossless source coding, channel capacity, and rate distortion theory. However, researchers in time series analysis and agent learning may not be familiar with these ideas. In this chapte

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Markov Chains and Cascaded Systems,ptions of systems using Markov chains play a dominant role in communications and compression applications. In this chapter, we provide a brief introduction to results from communications and compression but in a more general context. Plus, we relate the classical measure of performance in estimation

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Hypothesis Testing, Estimation, Information, and Sufficient Statistics,rning approaches also form statistics within their black boxes and in order to understand these methods, we need to consider what statistics those learning algorithms might be using. In both cases, we need to determine whether the statistic being used is sufficient to draw conclusions.

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Estimation and Entropy Power,information, except in the special case of Gaussian distributions. However, recent results have established a relationship between entropy power and minimum mean squared estimation error quantities. In this chapter, we developed these relationships and show that the change in mutual information obta