A Hasting-Metropolis algorithm with learning proposal

被引：3

作者：

Chauveau, D ^{[1
]}

Vandekerkhove, P ^{[1
]}

机构：

[1] Univ Marne La Vallee, F-77454 Marne La Vallee 2, France

来源：

COMPTES RENDUS DE L ACADEMIE DES SCIENCES SERIE I-MATHEMATIQUE | 1999年 / 329卷 / 02期

关键词：

D O I：

10.1016/S0764-4442(99)80484-4

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We present a non-homogeneous Hastings-Metropolis algorithm for which the proposal density approximates the target density, as the number of iterations increases. The proposal at the n-th step is a non-parametric estimate of the density of the algorithm, and uses an increasing number of i.i.d. copies of the Markov chain. The resulting algorithm converges (in n) geometrically faster than a Hastings-Metropolis algorithm with an arbitrary proposal. (C) Academie des Sciences/Elsevier, Paris.

引用

页码：173 / 176

页数：4