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Adaptive rejection sampling with fixed number of nodes

2017; Taylor & Francis; Volume: 48; Issue: 3 Linguagem: Inglês

10.1080/03610918.2017.1395039

1532-4141

Luca Martino, Francisco Louzada,

Bayesian Methods and Mixture Models

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.