পদ্ধতির তুলনা করুন

	বেয়েশীয় টেম্পোরাল নেটওয়ার্ক বিশ্লেষণ	বেয়েশীয় এক্সপোনেনশিয়াল র‍্যান্ডম গ্রাফ মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010s	2011
প্রবর্তক	Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)	Caimo, A., & Friel, N.
ধরন	Probabilistic generative model	Bayesian statistical model for networks
মৌলিক উৎস	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.
অপর নাম	Bayesian dynamic network analysis, Bayesian time-varying network model, BTNA, Bayesian longitudinal network analysis	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM
সম্পর্কিত	4	4
সারসংক্ষেপ	Bayesian temporal network analysis combines probabilistic Bayesian inference with time-ordered relational data to model how network structures evolve, quantify uncertainty around structural estimates, and make principled predictions about future connectivity patterns. It provides credible intervals on edge probabilities and community assignments rather than bare point estimates.	The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks.