A Metropolis-class sampler for targets with non-convex support

Autor:	Moriarty, John, Vogrinc, Jure, Zocca, Alessandro
Rok vydání:	2019
Předmět:	Mathematics - Probability Mathematics - Statistics Theory Statistics - Computation 65C05 62F12 (primary) 60F05 60J05 65C40 90C26 65K05 (secondary)
Druh dokumentu:	Working Paper
Popis:	We aim to improve upon the exploration of the general-purpose random walk Metropolis algorithm when the target has non-convex support $A \subset \mathbb{R}^d$, by reusing proposals in $A^c$ which would otherwise be rejected. The algorithm is Metropolis-class and under standard conditions the chain satisfies a strong law of large numbers and central limit theorem. Theoretical and numerical evidence of improved performance relative to random walk Metropolis are provided. Issues of implementation are discussed and numerical examples, including applications to global optimisation and rare event sampling, are presented. Comment: 18 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.09964 Zobrazit plný text záznamu View this record from Arxiv