Search results
Results From The WOW.Com Content Network
In probability theory and statistics, the Weibull distribution / ˈ w aɪ b ʊ l / is a continuous probability distribution. It models a broad range of random variables, largely in the nature of a time to failure or time between events. Examples are maximum one-day rainfalls and the time a user spends on a web page.
CDF of a bimodal Weibull distribution with Weibull Moduli of 4 and 10 and characteristic strengths of 40 and 120 MPa Examples of a bimodal Weibull PDF and CDF are plotted in the figures of this article with values of the characteristic strength being 40 and 120 MPa, the Weibull moduli being 4 and 10, and the value of Φ is 0.5, corresponding to ...
The finiteness of the weakest-link chain model causes major deviations from the Weibull distribution. As the structure size, measured by , increases, the grafting point of the Weibullian left part moves to the right until, at about =, the entire distribution becomes Weibullian. The mean strength can be computed from this distribution and, as it ...
The commonly-used Weibull distribution combines both of these effects, as do the log-normal and hypertabastic distributions. After modelling a given distribution and parameters for h ( t ) {\displaystyle h(t)} , the failure probability density f ( t ) {\displaystyle f(t)} and cumulative failure distribution F ( t ) {\displaystyle F(t)} can be ...
In probability theory and statistics, the generalized extreme value (GEV) distribution [2] is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions.
The Fréchet distribution, also known as inverse Weibull distribution, [2] [3] is a special case of the generalized extreme value distribution. It has the cumulative distribution function ( ) = > . where α > 0 is a shape parameter.
The Gumbel distribution is a particular case of the generalized extreme value distribution (also known as the Fisher–Tippett distribution). It is also known as the log-Weibull distribution and the double exponential distribution (a term that is alternatively sometimes used to refer to the Laplace distribution).
In statistics, the exponentiated Weibull family of probability distributions was introduced by Mudholkar and Srivastava (1993) as an extension of the Weibull family obtained by adding a second shape parameter. The cumulative distribution function for the exponentiated Weibull distribution is