Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model

被引：82

作者：

Wang, Bo ^{[1
]}

Titterington, D. M. ^{[2
]}

机构：

[1] Newcastle Univ, Sch Math & Stat, Newcastle Upon Tyne, Tyne & Wear, England

[2] Univ Glasgow, Dept Stat, Glasgow, Lanark, Scotland

来源：

BAYESIAN ANALYSIS | 2006年 / 1卷 / 03期

关键词：

Mixture model; Variational Bayes; Local convergence; Laplace approximation;

D O I：

10.1214/06-BA121

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper we propose a generalised iterative algorithm for calculating variational Bayesian estimates for a normal mixture model and investigate its convergence properties. It is shown theoretically that the variational Bayesian estimator converges locally to the maximum likelihood estimator at the rate of O (1/n) in the large sample limit.

引用

页码：625 / 649

页数：25

共 24 条

[1]

[Anonymous], 2005, P 10 INT WORKSH ART

[2]

Attias H, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P21

[3]

Attias H, 2000, ADV NEUR IN, V12, P209

[4]

Beal M.J., 2003, Variational algorithms for approximate Bayesian inference

[5]

Bhatia R., 2013, MATRIX ANAL

[6] Computational and inferential difficulties with mixture posterior distributions. [J].

Celeux, G ;

Hurn, M ;

Robert, CP .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2000, 95 (451) :957-970

[7]

Corduneanu C. Bishop, 2001, Artif. Intell. Statist., V18, P27

[8]

Duda R. O., 1973, PATTERN CLASSIFICATI

[9]

Evans MJ., 2000, APPROXIMATING INTEGR

[10]

Ghahramani Z, 2001, ADV NEUR IN, V13, P507

← 1 2 3 →