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  2. Inverse Gaussian distribution - Wikipedia

    en.wikipedia.org/wiki/Inverse_Gaussian_distribution

    The inverse Gaussian distribution has several properties analogous to a Gaussian distribution. The name can be misleading: it is an "inverse" only in that, while the Gaussian describes a Brownian motion's level at a fixed time, the inverse Gaussian describes the distribution of the time a Brownian motion with positive drift takes to reach a ...

  3. 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.

  4. Inverse distribution - Wikipedia

    en.wikipedia.org/wiki/Inverse_distribution

    The cumulative distribution function of the reciprocal, within the same range, is G ( y ) = b − y − 1 b − a . {\displaystyle G(y)={\frac {b-y^{-1}}{b-a}}.} For example, if X is uniformly distributed on the interval (0,1), then Y = 1 / X has density g ( y ) = y − 2 {\displaystyle g(y)=y^{-2}} and cumulative distribution function G ( y ...

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The probability density, cumulative distribution, and inverse cumulative distribution of any function of one or more independent or correlated normal variables can be computed with the numerical method of ray-tracing [41] (Matlab code). In the following sections we look at some special cases.

  6. Probit - Wikipedia

    en.wikipedia.org/wiki/Probit

    Plot of probit function. In probability theory and statistics, the probit function is the quantile function associated with the standard normal distribution.It has applications in data analysis and machine learning, in particular exploratory statistical graphics and specialized regression modeling of binary response variables.

  7. 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.

  8. Noncentral F-distribution - Wikipedia

    en.wikipedia.org/wiki/Noncentral_F-distribution

    The noncentral F-distribution is implemented in the R language (e.g., pf function), in MATLAB (ncfcdf, ncfinv, ncfpdf, ncfrnd and ncfstat functions in the statistics toolbox) in Mathematica (NoncentralFRatioDistribution function), in NumPy (random.noncentral_f), and in Boost C++ Libraries.

  9. Generalized inverse Gaussian distribution - Wikipedia

    en.wikipedia.org/wiki/Generalized_inverse...

    The inverse Gaussian and gamma distributions are special cases of the generalized inverse Gaussian distribution for p = −1/2 and b = 0, respectively. [7] Specifically, an inverse Gaussian distribution of the form