A rejection technique for sampling from T-concave distributions
ACM Transactions on Mathematical Software (TOMS)
Statistical analysis of extreme values
Statistical analysis of extreme values
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
An empirical study of collaborative acoustic source localization
Proceedings of the 6th international conference on Information processing in sensor networks
Gibbs sampling approach for generation of truncated multivariate Gaussian random variables
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
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Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling method is an efficient algorithm to sample from a log-concave target density, that attains high acceptance rates by improving the proposal density whenever a sample is rejected. In this paper we introduce a generalized adaptive rejection sampling procedure that can be applied with a broad class of target probability distributions, possibly non-log-concave and exhibiting multiple modes. The proposed technique yields a sequence of proposal densities that converge toward the target pdf, thus achieving very high acceptance rates. We provide a simple numerical example to illustrate the basic use of the proposed technique, together with a more elaborate positioning application using real data.