The distribution of the distance between a point and its nearest neighbour in a spatial Poisson process. Also the distribution of the distance from the origin in n-dimensional space to the point (X1, X2,…, Xn), where X1, X2,…, Xn are independent normal variables (see normal distribution), each with expected value 0 and variance σ2. The probability density function f is given by
where σ>0 and Γ is the gamma function. The distribution has mean μ and variance V given by 
The distribution has mode . In the case n=2, the expressions for the mean and variance simplify to and σ2(4−π) respectively.
Rayleigh distribution. The shape of the distribution depends on the value of the parameters σ and n. The figure illustrates the dependence on n, with σ=1.
- market potential
- market power
- market prices
- market risk
- markets
- market share
- market structure
- Markiewicz, Constance
- Markoffian process
- Markov, Andrei Andreevich (1856–1922)
- Markov, Andrey Andreyevich
- Markov chain
- Markov chain analysis
- Markov chain Monte Carlo
- Markov inequality
- Markovnikoff’s rule
- Markov process
- Markov property
- Markov random field
- Markov source
- Markov’s principle
- mark reading
- mark-recapture method
- mark-space ratio
- mark-to-market