**R – Xi'an's Og**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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**O**ne approach to random number generation that had always intrigued me is Kinderman and Monahan’s (1977) ratio-of-uniform method. The method is based on the result that the uniform distribution on the set A of (u,v)’s in **R⁺**x**X** such that

0≤u²≤**ƒ**(v/u)

induces the distribution with density proportional to **ƒ** on V/U. Hence the name. The proof is straightforward and the result can be seen as a consequence of the fundamental lemma of simulation, namely that simulating from the uniform distribution on the set B of (w,x)’s in **R⁺**x**X** such that

0≤w≤**ƒ**(x)

induces the marginal distribution with density proportional to **ƒ** on X. There is no mathematical issue with this result, but I have difficulties with picturing the construction of efficient random number generators based on this principle.

I thus took the opportunity of the second season of [the Warwick reading group on] Non-uniform random variate generation to look anew at this approach. (Note that the book is freely available on Luc Devroye’s website.) The first thing I considered is the shape of the set A. Which has nothing intuitive about it! Luc then mentions (p.195) that the boundary of A is given by

u(x)=√**ƒ**(x),v(x)=x√**ƒ**(x)

which then leads to bounding both **ƒ** and x→x²**ƒ**(x) to create a box around A and an accept-reject strategy, but I have trouble with this result without making further assumptions about **ƒ**… Using a two component normal mixture as a benchmark, I found bounds on u(.) and v(.) and simulated a large number of points within the box to end up with the above graph that indeed the accepted (u,v)’s were within this boundary. And the same holds with a more ambitious mixture:

Filed under: Books, pictures, R, Statistics Tagged: Luc Devroye, Non-Uniform Random Variate Generation, random number generation, ratio of uniform algorithm, University of Warwick

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