Parsimonious Adaptive Rejection Sampling
Abstract
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.
Cite
@article{arxiv.1710.04948,
title = {Parsimonious Adaptive Rejection Sampling},
author = {Luca Martino},
journal= {arXiv preprint arXiv:1710.04948},
year = {2017}
}
Comments
Related Matlab code can be found at http://www.lucamartino.altervista.org/PARS_CODE_v1.zip