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  2. Entropy in thermodynamics and information theory - Wikipedia

    en.wikipedia.org/wiki/Entropy_in_thermodynamics...

    where is the probability of the message taken from the message space M, and b is the base of the logarithm used. Common values of b are 2, Euler's number e, and 10, and the unit of entropy is shannon (or bit) for b = 2, nat for b = e, and hartley for b = 10. [1]

  3. Logarithm - Wikipedia

    en.wikipedia.org/wiki/Logarithm

    If a message recipient may expect any one of N possible messages with equal likelihood, then the amount of information conveyed by any one such message is quantified as log 2 N bits. [86] Lyapunov exponents use logarithms to gauge the degree of chaoticity of a dynamical system. For example, for a particle moving on an oval billiard table, even ...

  4. Entropy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(information_theory)

    The different units of information (bits for the binary logarithm log 2, nats for the natural logarithm ln, bans for the decimal logarithm log 10 and so on) are constant multiples of each other. For instance, in case of a fair coin toss, heads provides log 2 (2) = 1 bit of information, which is approximately 0.693 nats or 0.301 decimal digits.

  5. Information content - Wikipedia

    en.wikipedia.org/wiki/Information_content

    If the receiving entity had previously known the content of a message with certainty before receiving the message, the amount of information of the message received is zero. Only when the advance knowledge of the content of the message by the receiver is less than 100% certain does the message actually convey information.

  6. Binary logarithm - Wikipedia

    en.wikipedia.org/wiki/Binary_logarithm

    Binary logarithms can be used to calculate the length of the representation of a number in the binary numeral system, or the number of bits needed to encode a message in information theory. In computer science, they count the number of steps needed for binary search and related algorithms.

  7. List of logarithmic identities - Wikipedia

    en.wikipedia.org/wiki/List_of_logarithmic_identities

    The identities of logarithms can be used to approximate large numbers. Note that log b (a) + log b (c) = log b (ac), where a, b, and c are arbitrary constants. Suppose that one wants to approximate the 44th Mersenne prime, 2 32,582,657 −1. To get the base-10 logarithm, we would multiply 32,582,657 by log 10 (2), getting 9,808,357.09543 ...

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  9. Log probability - Wikipedia

    en.wikipedia.org/wiki/Log_probability

    The logarithm function is not defined for zero, so log probabilities can only represent non-zero probabilities. Since the logarithm of a number in (,) interval is negative, often the negative log probabilities are used. In that case the log probabilities in the following formulas would be inverted.