A Bayesian Approach for The Analysis of Triadic Data In Cognitive Social Structures. Issue 4 (30th January 2015)

• Record Type: Journal Article
• Authors: Swartz, Tim B.; Gill, Paramjit S.; Muthukumarana, Saman
• Is Part Of: Journal of the Royal Statistical Society. Volume 64:Issue 4(2015:Aug.)
• Page Start: 593
• Page End: 610
• Publication Date: 2015-01-30
• Subjects: Bayesian analyses -- Cognitive social structures -- Markov chain Monte Carlo methods -- Social network analysis -- Triadic relations -- WinBUGS; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12096
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: The paper proposes a fully Bayesian approach for the analysis of triadic data in social networks. Inference is based on Markov chain Monte Carlo methods as implemented in the software package WinBUGS. We apply the methodology to two data sets to highlight the ease with which cognitive social structures can be analysed.