[1] **viXra:1509.0048 [pdf]**
*replaced on 2017-10-08 05:28:48*

**Authors:** L. Martino, F. Louzada

**Comments:** 15 Pages. (to appear) Communications in Statistics - Simulation and Computation

The adaptive rejection sampling (ARS) algorithm is a universal
random generator for drawing samples eciently 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.

**Category:** Statistics