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  2. Unicity distance - Wikipedia

    en.wikipedia.org/wiki/Unicity_distance

    So for English each character can convey log 2 (26) = 4.7 bits of information. However the average amount of actual information carried per character in meaningful English text is only about 1.5 bits per character. So the plain text redundancy is D = 4.7 − 1.5 = 3.2. [1] Basically the bigger the unicity distance the better.

  3. Redundancy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Redundancy_(information...

    The quantity is called the relative redundancy and gives the maximum possible data compression ratio, when expressed as the percentage by which a file size can be decreased. (When expressed as a ratio of original file size to compressed file size, the quantity R : r {\displaystyle R:r} gives the maximum compression ratio that can be achieved.)

  4. Redundant proof - Wikipedia

    en.wikipedia.org/wiki/Redundant_proof

    The second pattern of potentially globally redundant proofs appearing in global redundancy definition is related to the well-known [further explanation needed] notion of regularity [further explanation needed]. Informally, a proof is irregular if there is a path from a node to the root of the proof such that a literal is used more than once as ...

  5. Interaction information - Wikipedia

    en.wikipedia.org/wiki/Interaction_information

    There are many names for interaction information, including amount of information, [1] information correlation, [2] co-information, [3] and simply mutual information. [4] Interaction information expresses the amount of information (redundancy or synergy) bound up in a set of variables, beyond that which is present in any subset of those variables.

  6. Tautology (rule of inference) - Wikipedia

    en.wikipedia.org/wiki/Tautology_(rule_of_inference)

    Theorems are those logical formulas where is the conclusion of a valid proof, [4] while the equivalent semantic consequence indicates a tautology. The tautology rule may be expressed as a sequent :

  7. Binary symmetric channel - Wikipedia

    en.wikipedia.org/wiki/Binary_symmetric_channel

    Theorem — If ⌈ ((+)) ⌉ then the following is true for every encoding and decoding function : {,} {,} and : {,} {,} respectively: [(() +)]. The intuition behind the proof is however showing the number of errors to grow rapidly as the rate grows beyond the channel capacity.

  8. Consensus theorem - Wikipedia

    en.wikipedia.org/wiki/Consensus_theorem

    In Boolean algebra, the consensus theorem or rule of consensus [1] is the identity: ¯ = ¯ The consensus or resolvent of the terms and ¯ is . It is the conjunction of all the unique literals of the terms, excluding the literal that appears unnegated in one term and negated in the other.

  9. Karush–Kuhn–Tucker conditions - Wikipedia

    en.wikipedia.org/wiki/Karush–Kuhn–Tucker...

    Theorem — (sufficiency) If there exists a solution to the primal problem, a solution (,) to the dual problem, such that together they satisfy the KKT conditions, then the problem pair has strong duality, and , (,) is a solution pair to the primal and dual problems.