Transformed density rejection with inflection points
The acceptance-rejection algorithm is often used to sample from non-standard distributions. For this algorithm to be efficient, however, the user has to create a hat function that majorizes and closely matches the density of the distribution to be sampled from. There are many methods for automatically creating such hat functions, but these methods require that the user transforms the density so that she knows the exact location of the transformed density's inflection points. In this paper, we propose an acceptance-rejection algorithm which obviates this need and can thus be used to sample from a larger class of distributions.
transformed density rejection inflection points nonuniform random variate generation