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  1. Information (x) = -log (p (x))
    We can calculate the amount of information there is in an event using the probability of the event. This is called “ Shannon information,” “ self-information,” or simply the “ information,” and can be calculated for a discrete event x as follows: information (x) = -log (p (x))

    A Gentle Introduction to Information Entropy

    machinelearningmastery.com/what-is-information-entropy/
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  2. 其他用户还问了以下问题
    What are the fundamental quantities in in-formation theory?
    In this note, we first discuss how to formulate the main fundamental quantities in In-formation Theory: information, Shannon entropy and channel capacity. We then present the derivation of the classical capacity formula under the channel with additive white Gaussian noise (AWGN).

    A Brief Introduction to Shannon’s Information Theory - arXiv.org

    arxiv.org
    How do you calculate information?
    The calculation of information is often written as h (); for example: The negative sign ensures that the result is always positive or zero. Information will be zero when the probability of an event is 1.0 or a certainty, e.g. there is no surprise. Let’s make this concrete with some examples.

    A Gentle Introduction to Information Entropy

    machinelearningmastery.com
    What is the intuition behind quantifying information?
    The intuition behind quantifying information is the idea of measuring how much surprise there is in an event. Those events that are rare (low probability) are more surprising and therefore have more information than those events that are common (high probability). Low Probability Event: High Information (surprising).

    A Gentle Introduction to Information Entropy

    machinelearningmastery.com
    Can We define the amount of information of each letter?
    This suggests that we can actually define the amount of information of each letter. Here, we say “average” because we think the amount of information that different letters have should be different as they may not “contribute equally” in a sequence, depending on the respective probabilities of the letters.

    A Brief Introduction to Shannon’s Information Theory - arXiv.org

    arxiv.org
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    Wikipedia
    https://en.wikipedia.org › ... › Quantities_of_informa…

    Quantities of information - Wikipedia

    The mathematical theory of information is based on probability theory and statistics, and measures information with several quantities of information. The choice of logarithmic base in the following formulae determines the unit of information entropy that is used. The most common unit of information is the bit, or more … 展开

    Self-information

    Shannon derived a measure of information content called the self-information or "surprisal" of a message $${\displaystyle m}$$:
    where 展开

    Mutual information (transinformation)

    It turns out that one of the most useful and important measures of information is the mutual information, or transinformation. This is a measure of how much information can be … 展开

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  4. Machine Learning Mastery
    https://machinelearningmastery.com › what-is-information-entropy

    A Gentle Introduction to Information Entropy

  5. Wikipedia
    https://en.wikipedia.org › wiki › Entropy_(information_theory)

    Entropy (information theory) - Wikipedia

  6. Brilliant
    https://brilliant.org › wiki › entropy-information-theory

    Entropy (Information Theory) | Brilliant Math & Science Wiki

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    Information theory
    Information Entropy Example
  7. Stanford Electrical Engineering
    https://ee.stanford.edu › ~gray › it.pdf
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    Entropy and Information Theory - Stanford University …

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    Information theory
    Information and Entropy
    Mutual Information Entropy
  8. tufts.edu
    http://www.ece.tufts.edu › ee
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    Lecture 1: Entropy and mutual information - Tufts University

  9. arXiv.org
    https://arxiv.org › pdf
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    A Brief Introduction to Shannon’s Information Theory - arXiv.org

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    Information Theory
    Information and Entropy
    Quantities of information
  10. Towards Data Science
    https://towardsdatascience.com

    Essential Math for Data Science: Information Theory

    网页2020年12月2日 · The first step to understanding information theory is to consider the concept of the quantity of information associated with a random variable. In information theory, this quantity of information is …

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    Information Theory
    Mathematics
  11. Mathematics Stack Exchange
    https://math.stackexchange.com › questions

    information theory - Intuitive explanation of entropy

    网页2013年3月15日 · The answer is very simple and extremely intuitive: how about we measure the quantity of information based on the number of questions that must be asked in order to fully discover all unknowns that …

    stochastic processes - How to prove a differential entropy ...2020年12月14日
    information theory - Shannon's formula
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  12. princeton.edu
    http://pillowlab.princeton.edu › teaching › slides
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    Lecture 8: Information Theory and Maximum Entropy

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    Information Theory
    Information and Entropy
    Maximum Entropy Bernoulli

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