# Logarithmic distribution

Parameters Probability mass function The function is only defined at integer values. The connecting lines are merely guides for the eye. Cumulative distribution function $0 < p < 1\!$ $k \in \{1,2,3,\dots\}\!$ $\frac{-1}{\ln(1-p)} \; \frac{\;p^k}{k}\!$ $1 + \frac{\Beta(p;k+1,0)}{\ln(1-p)}\!$ $\frac{-1}{\ln(1-p)} \; \frac{p}{1-p}\!$ $1$ $-p \;\frac{p + \ln(1-p)}{(1-p)^2\,\ln^2(1-p)} \!$ $\frac{\ln(1 - p\,\exp(t))}{\ln(1-p)}\text{ for }t<-\ln p\,$ $\frac{\ln(1 - p\,\exp(i\,t))}{\ln(1-p)}\text{ for }t\in\mathbb{R}\!$ $\frac{\ln(1-pz)}{\ln(1-p)}\text{ for }|z|<\frac1p$

In probability and statistics, the logarithmic distribution (also known as the logarithmic series distribution or the log-series distribution) is a discrete probability distribution derived from the Maclaurin series expansion

$-\ln(1-p) = p + \frac{p^2}{2} + \frac{p^3}{3} + \cdots.$

From this we obtain the identity

$\sum_{k=1}^{\infty} \frac{-1}{\ln(1-p)} \; \frac{p^k}{k} = 1.$

This leads directly to the probability mass function of a Log(p)-distributed random variable:

$f(k) = \frac{-1}{\ln(1-p)} \; \frac{p^k}{k}$

for k ≥ 1, and where 0 < p < 1. Because of the identity above, the distribution is properly normalized.

$F(k) = 1 + \frac{\Beta(p; k+1,0)}{\ln(1-p)}$

where B is the incomplete beta function.

A Poisson compounded with Log(p)-distributed random variables has a negative binomial distribution. In other words, if N is a random variable with a Poisson distribution, and Xi, i = 1, 2, 3, ... is an infinite sequence of independent identically distributed random variables each having a Log(p) distribution, then

$\sum_{i=1}^N X_i$

has a negative binomial distribution. In this way, the negative binomial distribution is seen to be a compound Poisson distribution.

R.A. Fisher described the logarithmic distribution in a paper that used it to model relative species abundance.[1]

$\left\{(k+1) \Pr (k+1)-k p \Pr (k)=0,\Pr (1)=-\frac{p}{\log (1-p)}\right\}$