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  2. Quantities of information - Wikipedia

    en.wikipedia.org/wiki/Quantities_of_information

    That can be derived using this definition by considering a compound message & providing information regarding the values of two random variables M and N using a message which is the concatenation of the elementary messages m and n, each of whose information content are given by ⁡ and ⁡ respectively.

  3. Uncertainty quantification - Wikipedia

    en.wikipedia.org/wiki/Uncertainty_quantification

    Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.

  4. Burst error-correcting code - Wikipedia

    en.wikipedia.org/wiki/Burst_error-correcting_code

    We are allowed to do so, since Fire Codes operate on . By our assumption, v ( x ) {\displaystyle v(x)} is a valid codeword, and thus, must be a multiple of g ( x ) {\displaystyle g(x)} . As mentioned earlier, since the factors of g ( x ) {\displaystyle g(x)} are relatively prime, v ( x ) {\displaystyle v(x)} has to be divisible by x 2 ℓ − 1 ...

  5. Observational error - Wikipedia

    en.wikipedia.org/wiki/Observational_error

    If you consider an experimenter taking a reading of the time period of a pendulum swinging past a fiducial marker: If their stop-watch or timer starts with 1 second on the clock then all of their results will be off by 1 second (zero error).

  6. Information theory - Wikipedia

    en.wikipedia.org/wiki/Information_theory

    (Here, I(x) is the self-information, which is the entropy contribution of an individual message, and is the expected value.) A property of entropy is that it is maximized when all the messages in the message space are equiprobable p(x) = 1/n; i.e., most unpredictable, in which case H(X) = log n.

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  8. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables ⁡ (+) = ⁡ + ⁡ + ⁡ (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...

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