Linganisha mbinu

	Uchanganuzi wa Mitandao ya Kijamii wa Kibayesiani	Uchambuzi wa Mitandao ya Kijamii
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2002	1934 (sociometry); 1994 (modern formalization)
Mwanzilishi	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.	Moreno, J.L.; formalized by Wasserman & Faust
Aina	Probabilistic / Bayesian network model	Structural/relational analysis framework
Chanzo asilia	Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Majina mbadala	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling	SNA, network analysis, sociometric analysis, relational analysis
Zinazohusiana	5	5
Muhtasari	Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.