Concave-Convex Adaptive Rejection Sampling
We describe a method for generating independent samples from univariate density functions using adaptive rejection sampling without the log-concavity requirement. The method makes use of the fact that many functions can be expressed as a sum of con- cave and convex functions. Using a concave-convex decomposition, we bound the log-density by separately bounding the concave and convex parts using piecewise linear functions. The upper bound can then be used as the proposal distribution in rejection sampling. We demonstrate the applicability of the concave-convex approach on a number of standard distributions and describe an application to the efficient construction of sequential Monte Carlo proposal distributions for inference over genealogical trees. Computer code for the proposed algorithms is available online.