পদ্ধতির তুলনা করুন

	বেয়েশীয় এক্সপোনেনশিয়াল র‍্যান্ডম গ্রাফ মডেল	বেইসিয়ান স্টোকাস্টিক ব্লক মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2011	2001–2014
প্রবর্তক	Caimo, A., & Friel, N.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
ধরন	Bayesian statistical model for networks	Probabilistic generative model with Bayesian inference
মৌলিক উৎস	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
অপর নাম	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.