.. _variables-frechet_uncertain: """"""""""""""""" frechet_uncertain """"""""""""""""" Aleatory uncertain variable - Frechet **Topics** continuous_variables, aleatory_uncertain_variables .. toctree:: :hidden: :maxdepth: 1 variables-frechet_uncertain-alphas variables-frechet_uncertain-betas variables-frechet_uncertain-initial_point variables-frechet_uncertain-descriptors **Specification** - *Alias:* None - *Arguments:* INTEGER - *Default:* no frechet uncertain variables **Child Keywords:** +-------------------------+--------------------+--------------------+-----------------------------------------------+ | Required/Optional | Description of | Dakota Keyword | Dakota Keyword Description | | | Group | | | +=========================+====================+====================+===============================================+ | Required | `alphas`__ | First parameter of the Frechet distribution | +----------------------------------------------+--------------------+-----------------------------------------------+ | Required | `betas`__ | Second parameter of the Frechet distribution | +----------------------------------------------+--------------------+-----------------------------------------------+ | Optional | `initial_point`__ | Initial values for variables | +----------------------------------------------+--------------------+-----------------------------------------------+ | Optional | `descriptors`__ | Labels for the variables | +----------------------------------------------+--------------------+-----------------------------------------------+ .. __: variables-frechet_uncertain-alphas.html __ variables-frechet_uncertain-betas.html __ variables-frechet_uncertain-initial_point.html __ variables-frechet_uncertain-descriptors.html **Description** The Frechet distribution is also referred to as the Type II Largest Extreme Value distribution. The distribution of maxima in sample sets from a population with a lognormal distribution will asymptotically converge to this distribution. It is commonly used to model non-negative demand variables. The density function for the frechet distribution is: .. math:: f(x) = \frac{\alpha}{\beta} \left( \frac{\beta}{x} \right)^{\alpha+1} \exp \left( -\left(\frac{\beta}{x}\right)^\alpha \right), where :math:`\mu = \beta\Gamma(1-\frac{1}{\alpha}),` and :math:`\sigma^2 = \beta^2[\Gamma(1-\frac{2}{\alpha})-\Gamma^2(1-\frac{1}{\alpha})]` **Theory** When used with some methods such as design of experiments and multidimensional parameter studies, distribution bounds are inferred to be [0, :math:`\mu + 3 \sigma` ]. For some methods, including vector and centered parameter studies, an initial point is needed for the uncertain variables. When not given explicitly, these variables are initialized to their means.