A SEMIPARAMETRIC BAYESIAN APPROACH TO NETWORK MODELLING USING DIRICHLET PROCESS PRIOR DISTRIBUTIONS

被引：6

作者：

Ghosh, Pulak ^{[2
]}

Gill, Paramjit ^{[3
]}

Muthukumarana, Saman

Swartz, Tim ^{[1
]}

机构：

[1] Simon Fraser Univ, Dept Stat & Actuarial Sci, Burnaby, BC V5A 1S6, Canada

[2] Indian Inst Management, Dept Quantitat Methods & Informat Syst, Bangalore 560076, Karnataka, India

[3] Univ British Columbia Okanagan, Math Stat & Phys Unit, Irving K Barber Sch Arts & Sci, Kelowna, BC V1V 1V7, Canada

来源：

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS | 2010年 / 52卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Bayesian semiparametric modelling; clustering; Dirichlet process; network models; social relations; WinBUGS software; ROUND ROBIN ANALYSIS; SOCIAL NETWORKS; DIRECTED-GRAPHS; MARKOV GRAPHS; VARIANCE; FAMILY;

D O I：

10.1111/j.1467-842X.2010.00583.x

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

P>This paper considers the use of Dirichlet process prior distributions in the statistical analysis of network data. Dirichlet process prior distributions have the advantages of avoiding the parametric specifications for distributions, which are rarely known, and of facilitating a clustering effect, which is often applicable to network nodes. The approach is highlighted for two network models and is conveniently implemented using WinBUGS software.

引用

页码：289 / 302

页数：14

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