The Geometric distribution can be thought of as a generalization of the Bernoulli() distribution where we ask: "if I keep flipping a coin with probability p of heads, what is the probability I need $$k$$ flips before I get my first heads?" The Geometric distribution is a special case of Negative Binomial distribution.

Geometric(p = 0.5)

## Arguments

p The success probability for the distribution. p can be any value in [0, 1], and defaults to 0.5.

## Value

A Geometric object.

## Details

We recommend reading this documentation on https://alexpghayes.github.io/distributions3, where the math will render with additional detail and much greater clarity.

In the following, let $$X$$ be a Geometric random variable with success probability p = $$p$$. Note that there are multiple parameterizations of the Geometric distribution.

Support: 0 < p < 1, $$x = 0, 1, \dots$$

Mean: $$\frac{1-p}{p}$$

Variance: $$\frac{1-p}{p^2}$$

Probability mass function (p.m.f):

$$P(X = x) = p(1-p)^x,$$

Cumulative distribution function (c.d.f):

$$P(X \le x) = 1 - (1-p)^{x+1}$$

Moment generating function (m.g.f):

$$E(e^{tX}) = \frac{pe^t}{1 - (1-p)e^t}$$

Other discrete distributions: Bernoulli, Binomial, Categorical, HyperGeometric, Multinomial, NegativeBinomial, Poisson

## Examples


set.seed(27)

X <- Geometric(0.3)
X#> Geometric distribution (p = 0.3)
random(X, 10)#>   0 1 9 2 4 6 4 2 3 1
pdf(X, 2)#>  0.147log_pdf(X, 2)#>  -1.917323
cdf(X, 4)#>  0.83193quantile(X, 0.7)#>  3