  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 technique which draw efficiently independent samples from
univariate target densities. The ARS schemes yield a sequence of proposal
functions that converge toward 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. We propose the Parsimonious
Adaptive Rejection Sampling (PARS) method, where an efficient trade-off between
acceptance rate and proposal complexity is obtained. Thus, the resulting
algorithm is faster than the standard ARS approach.
