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 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.

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

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Submission history

[v1] 2015-09-04 05:40:14
[v2] 2017-10-08 05:28:48

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