2012
Martino, Luca; Olmo, Victor Pascual Del; Read, Jesse
A Multi-Point Metropolis Scheme with Generic Weight Functions Artículo de revista
En: Statistics & Probability Letters, vol. 82, no 7, pp. 1445–1453, 2012.
Resumen | Enlaces | BibTeX | Etiquetas: MCMC methods, Multi-point Metropolis algorithm, Multiple Try Metropolis algorithm
@article{Martino2012,
title = {A Multi-Point Metropolis Scheme with Generic Weight Functions},
author = {Luca Martino and Victor Pascual Del Olmo and Jesse Read},
url = {http://www.sciencedirect.com/science/article/pii/S0167715212001514},
year = {2012},
date = {2012-01-01},
journal = {Statistics \& Probability Letters},
volume = {82},
number = {7},
pages = {1445--1453},
abstract = {The multi-point Metropolis algorithm is an advanced MCMC technique based on drawing several correlated samples at each step and choosing one of them according to some normalized weights. We propose a variation of this technique where the weight functions are not specified, i.e., the analytic form can be chosen arbitrarily. This has the advantage of greater flexibility in the design of high-performance MCMC samplers. We prove that our method fulfills the balance condition, and provide a numerical simulation. We also give new insight into the functionality of different MCMC algorithms, and the connections between them.},
keywords = {MCMC methods, Multi-point Metropolis algorithm, Multiple Try Metropolis algorithm},
pubstate = {published},
tppubtype = {article}
}
The multi-point Metropolis algorithm is an advanced MCMC technique based on drawing several correlated samples at each step and choosing one of them according to some normalized weights. We propose a variation of this technique where the weight functions are not specified, i.e., the analytic form can be chosen arbitrarily. This has the advantage of greater flexibility in the design of high-performance MCMC samplers. We prove that our method fulfills the balance condition, and provide a numerical simulation. We also give new insight into the functionality of different MCMC algorithms, and the connections between them.