**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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**B**eing still puzzled (!) by the ratio-of-uniform approach, mostly failing to catch its relevance for either standard distributions in a era when computing a cosine or an exponential is negligible, or non-standard distributions for which computing bounds and boundaries is out-of-reach, I kept searching for solutions that would include unbounded densities and still produce compact boxes, as this seems essential for accept-reject simulation if not for slice sampling. And after exploring some dead-ends (in tune with running in Venezia!), I came upon the case of the generalised logistic transform

which ensures that the [ratio-of-almost-uniform] set I defined in my slides last week

is bounded in u. Since the transform g is the derivative of the inverse of h (!),

the parametrisation of the boundary of * H* is

which means it remains bounded if (a) a≤1 [to ensure boundedness at infinity] and (b) the limit of v(x) at zero [where I assume the asymptote stands] is bounded. Meaning

For instance, this holds for Gamma distributions with shape parameter larger than ½…

Working a wee bit more on the problem led me to realise that resorting an arbitrary cdf Φ instead of the logistic one could solve the problem for most distributions, including all Gammas. Indeed, the boundary of * H* is now

which means it remains bounded if φ has very heavy tails, like 1/x². To handle the explosion when x=0. And an asymptote itself at zero, to handle the limit at infinity when f(x) goes to zero.

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

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