P-Distribution

< Back to Probability Distribution List The term “inverse normal distribution” is informal and doesn’t refer to a specific probability distribution [1]. However, there is an inverse normal density function in statistics that reverses the procedure for finding z-values (see “Inverse Normal Distribution” below). There is a generalized inverse normal distribution family, proposed by Robert […]

< Back to Probability Distribution List An asymptotic normal distribution is one that exhibits a property called asymptotic normality. Asymptotic normality is a property of an estimator (like the sample mean or sample standard deviation). The term “Asymptotic” refers to how the estimator behaves as the sample size tends to infinity; an estimator that has […]

< Back to Probability Distribution List The random variables that make up the log-normal distribution have normally distributed logarithms. As Y = ln(x) only exists for positive values of x, log-normal distributions have all-positive values. The log-normal distribution is particularly suited to fit skewed data with low mean and large variance. This occurs frequently in real life, […]

< Back to Probability Distributions List A mixture distribution is a distribution with two or more combined probability distributions; A new distribution is created by drawing random variables from two or more parent. The parent populations can be univariate distributions or multivariate distributions. The mixture distributions should be comprised of distributions with the same dimension. […]

< Back to Probability Distribution List A normal distribution, denoted Ν (μ, σ2) is a symmetrical, bell-shaped distribution. It’s widely used in business and statistics because many real-life phenomena fit a bell-curve shape like heights of people, blood pressure readings, or standardized test scores like the SAT. The empirical rule, depicted above, tells you what percentage of normally […]

< Back to List of Distributions A triangular distribution (or triangle distribution) is a continuous probability distribution defined by three parameters: a: the minimum or lower limit, (a ≤ c), c: the mode (height or peak), (a ≤ c ≤ b), b: the maximum or upper limit (b ≥ c). When a and b are equal but opposite in sign […]

< Back to Probability Distribution List The Cauchy distribution (also called the Lorentz distribution, Cauchy–Lorentz distribution, Lorentz(ian) function, or Breit–Wigner distribution) is a family of continuous probably distributions named after Augustin Cauchy. They resemble the normal distribution with a taller peak. Unlike the normal distribution, its fat tails decay much more slowly. The distribution, which describes resonance behavior, is well known for the fact […]

< Back to Probability Distributions List The 1962 National Bureau of Standards Report lists the Kapetyn distribution as another name for a generalized normal distribution. There are sparse references to the Kapetyn distribution as a synonym for the “generalized” normal outside of the NBS report. The generalized family of normal distributions is a very large […]

< Back to Probability Distributions List The Stuttering Poisson Distribution (SPD) is a non-negative discrete compound Poisson distribution that describes two or more events that happen in quickly in bursts. For example, the events might occur in groups or batches [1]. The distribution has the probability generating function (PGF): Where Px is the probability a […]

< Back to Probability Distribution List The Pollaczek-Geiringer distribution is another name for the stuttering Poisson Distribution (SPD), a non-negative discrete compound probability distribution [1]. Historical Notes on the Pollaczek-Geiringer Distribution The Pollaczek-Geiringer distribution makes a sparse entry in [2] The reference [17] No. 9 refers to the obscure and seldom-reference book A Summary of […]