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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978-3-031-65390-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Information Theoretic Principles for Agent Learning978-3-031-65388-9Series ISSN 2690-0300 Series E-ISSN 2690-0327

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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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Entropy and Mutual Information,y in these fundamental results that motivates further study and investigations. As an initial sequence model, we consider a discrete random variable . that takes on the values ., where the set of possible values of . is often called the ., denoted as U, and the elements of the set are called . of the alphabet.

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2690-0300 tions and compression fundamental limits. Readers will benefit from the presentation of information theoretic quantities, definitions, and results that provide or could provide insights into data science and learning..978-3-031-65390-2978-3-031-65388-9Series ISSN 2690-0300 Series E-ISSN 2690-0327