Parsimonious adaptive rejection sampling

Monte Carlo (MC) methods have become very popular in signal processing during the past decades. The adaptive rejection sampling (ARS) algorithms are well‐known MC techniques which draw efficiently independent samples from univariate target densities. The ARS schemes yield a sequence of proposal functions that converge towards the target, so that the probability of accepting a sample approaches one. However, sampling from the proposal pdf becomes more computationally demanding each time it is updated. The parsimonious ARS method, where an efficient trade‐off between acceptance rate and proposal complexity is obtained, is proposed. Thus, the resulting algorithm is faster than the standard ARS approach.