@inproceedings{c5f3edbfd0f84dd2aa71f07cecf41783,
title = "Independent doubly Adaptive Rejection Metropolis Sampling",
abstract = "Adaptive Rejection Metropolis Sampling (ARMS) is a well-known MCMC scheme for generating samples from one-dimensional target distributions. ARMS is widely used within Gibbs sampling, where automatic and fast samplers are often needed to draw from univariate full-conditional densities. In this work, we propose an alternative adaptive algorithm (IA2RMS) that overcomes the main drawback of ARMS (an uncomplete adaptation of the proposal in some cases), speeding up the convergence of the chain to the target. Numerical results show that IA2RMS outperforms the standard ARMS, providing a correlation among samples close to zero.",
keywords = "Gibbs sampler, Monte Carlo methods, adaptive rejection Metropolis sampling (ARMS)",
author = "Luca Martino and Jesse Read and David Luengo",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICASSP.2014.6855158",
language = "English",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7998--8002",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}