| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo, Inst Math Sci & Comp, BR-05508070 Sao Paulo - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 48, n. 3, p. 655-665, MAR 16 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 14/23160-6 - Efficient Monte Carlo sampling for high-volume spaces and large medical and industrial databases |
| Grantee: | Luca Martino |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |