> For the complete documentation index, see [llms.txt](https://math-5.gitbook.io/math/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://math-5.gitbook.io/math/reference/statistics.md). # Statistics maTH library also supports some distributions from statistics. Here's a list of them all:

Distribution	Description	Source
`Binomial(n, p)`	Number of successes in a sequence of n independent experiments, all with constant probability of p.	Binomial
`Poisson(lambda)`	Expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate (lambda) and independently of the time since the last event.	Poisson
`Geometric(p)`	Describes when the first success in an infinite sequence of independent and identically distributed Bernoulli trials occurs.	Geometric
`Pascal(r, p)`	Also known as negative binomial distribution, expresses the number of failures in a sequence of independent and identically distributed Bernoulli trials before a specified number of successes (r) occurs.	Pascal
`Hypergeometric(K, N, n)`	Describes the probability of k successes in n draws, without replacement, from a finite population of size N that contains exactly K objects with that feature, wherein each draw is either a success or a failure.	Hypergeometric
`Exponential(lambda)`	The probability distribution of the distance between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate (lambda)	Exponential
`Uniform(a, b)`	Such a distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds [a, b].	Uniform