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  2. Mean absolute error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_error

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  3. Symmetric mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Symmetric_mean_absolute...

    The earliest reference to a similar formula appears to be Armstrong (1985, p. 348), where it is called "adjusted MAPE" and is defined without the absolute values in the denominator. It was later discussed, modified, and re-proposed by Flores (1986).

  4. Mean absolute scaled error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_scaled_error

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  5. 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 ...

  6. Error metric - Wikipedia

    en.wikipedia.org/wiki/Error_metric

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  7. Mean percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_percentage_error

    Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero.

  8. Microsoft Office XML formats - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Office_XML_formats

    XML Spreadsheet documents cannot store Visual Basic for Applications macros, auditing tracer arrows, charts and other graphic objects, custom views, drawing object layers, outlining, scenarios, shared workbook information and user-defined function categories. [1] In contrast, the newer Office Open XML formats support full document fidelity.

  9. Bayes estimator - Wikipedia

    en.wikipedia.org/wiki/Bayes_estimator

    The Bayes risk of ^ is defined as ((, ^)), where the expectation is taken over the probability distribution of : this defines the risk function as a function of ^. An estimator θ ^ {\displaystyle {\widehat {\theta }}} is said to be a Bayes estimator if it minimizes the Bayes risk among all estimators.