| 书目名称 | New Foundations for Information Theory | | 副标题 | Logical Entropy and | | 编辑 | David Ellerman | | 视频video | | | 概述 | Introduces a new foundation for information theory, based on logical entropy & its transform into Shannon entropy.Offers a new maximizing logical entropy approach to the MaxEntropy method.Presents a n | | 丛书名称 | SpringerBriefs in Philosophy | | 图书封面 |  | | 描述 | This monograph offers a new foundation for information theory that is based on the notion of information-as-distinctions, being directly measured by logical entropy, and on the re-quantification as Shannon entropy, which is the fundamental concept for the theory of coding and communications..Information is based on distinctions, differences, distinguishability, and diversity. Information sets are defined that express the distinctions made by a partition, e.g., the inverse-image of a random variable so they represent the pre-probability notion of information. Then logical entropy is a probability measure on the information sets, the probability that on two independent trials, a distinction or “dit” of the partition will be obtained. .The formula for logical entropy is a new derivation of an old formula that goes back to the early twentieth century and has been re-derived many times in different contexts. As a probability measure, all the compound notions of joint, conditional, and mutual logical entropy are immediate. The Shannon entropy (which is not defined as a measure in the sense of measure theory) and its compound notions are then derived from a non-linear dit-to-bit transfor | | 出版日期 | Book 2021 | | 关键词 | entropy; partition logic; information theory; logical entropy; Shannon entropy; logical probability; index | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-86552-8 | | isbn_softcover | 978-3-030-86551-1 | | isbn_ebook | 978-3-030-86552-8Series ISSN 2211-4548 Series E-ISSN 2211-4556 | | issn_series | 2211-4548 | | copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 |
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发表于 2025-3-21 18:36:29
