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  2. Histogram matching - Wikipedia

    en.wikipedia.org/wiki/Histogram_matching

    A transformation of p r (r) is needed to convert it to p z (z). Input image CDF matched to desired output CDF. Each pdf (probability density function) can easily be mapped to its cumulative distribution function by = = (), =,,,, …,

  3. Computable Document Format - Wikipedia

    en.wikipedia.org/wiki/Computable_Document_Format

    Computable Document Format (CDF) is an electronic document format [1] designed to allow authoring dynamically generated, interactive content. [2] CDF was created by Wolfram Research , and CDF files can be created using Mathematica . [ 3 ]

  4. Empirical distribution function - Wikipedia

    en.wikipedia.org/wiki/Empirical_distribution...

    In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. [1] This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified ...

  5. Continuous uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Continuous_uniform...

    The uniform distribution is useful for sampling from arbitrary distributions. A general method is the inverse transform sampling method, which uses the cumulative distribution function (CDF) of the target random variable. This method is very useful in theoretical work.

  6. CDF-based nonparametric confidence interval - Wikipedia

    en.wikipedia.org/wiki/CDF-based_nonparametric...

    In statistics, cumulative distribution function (CDF)-based nonparametric confidence intervals are a general class of confidence intervals around statistical functionals of a distribution. To calculate these confidence intervals, all that is required is an independently and identically distributed (iid) sample from the distribution and known ...

  7. Quantile function - Wikipedia

    en.wikipedia.org/wiki/Quantile_function

    The quantile function, Q, of a probability distribution is the inverse of its cumulative distribution function F. The derivative of the quantile function, namely the quantile density function, is yet another way of prescribing a probability distribution. It is the reciprocal of the pdf composed with the quantile function.