Adaptive Rejection Sampling with Fixed Number of Nodes

Authors: L. Martino, F. Louzada

The adaptive rejection sampling (ARS) algorithm is a universal random generator for drawing samples efficiently from a univariate log-concave target probability density function (pdf). ARS generates independent samples from the target via rejection sampling with high acceptance rates. Indeed, ARS yields a sequence of proposal functions that converge toward the target pdf, so that the probability of accepting a sample approaches one. However, sampling from the proposal pdf becomes more computational demanding each time it is updated. In this work, we propose a novel ARS scheme, called Cheap Adaptive Rejection Sampling (CARS), where the computational effort for drawing from the proposal remains constant, decided in advance by the user. For generating a large number of desired samples, CARS is faster than ARS.

Comments: 13 Pages.

Download: PDF

Submission history

[v1] 2015-09-04 05:40:14

Unique-IP document downloads: 68 times

Articles available on are pre-prints that may not yet have been verified by peer-review and should therefore be treated as preliminary and speculative. Nothing stated should be treated as sound unless confirmed and endorsed by a concensus of independent qualified experts. In particular anything that appears to include financial or legal information or proposed medical treatments should not be taken as such. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus