Parsimonious Adaptive Rejection Sampling

TL;DR

The paper introduces PARS, a more efficient adaptive rejection sampling method that balances acceptance rate and proposal complexity, resulting in faster sampling from target distributions.

Contribution

PARS provides a novel trade-off mechanism in adaptive rejection sampling, improving computational efficiency over standard ARS methods.

Findings

PARS achieves higher sampling speed than standard ARS.

The method maintains high acceptance rates with reduced proposal complexity.

Experimental results demonstrate improved efficiency in signal processing applications.

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.