Search results
Results From The WOW.Com Content Network
In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted (), is a family of continuous multivariate probability distributions parameterized by a vector of positive reals.
In probability theory and statistics, Student's t distribution (or simply the t distribution) is a continuous probability distribution that generalizes the standard normal distribution.
Born in Lübeck, Radbruch studied law in Munich, Leipzig and Berlin.He passed his first bar exam ("Staatsexamen") in Berlin in 1901, and the following year he received his doctorate with a dissertation on "The Theory of Adequate Causation".
In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws, without replacement, from a finite population of size that contains exactly objects with that feature, wherein each draw is either a success or a failure.
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.
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is = (). [citation needed]
Cumulative distribution function for the exponential distribution Cumulative distribution function for the normal distribution. In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable, or just distribution function of , evaluated at , is the probability that will take a value less than or equal to .
In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution.