Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Beam sampling for the infinite hidden Markov model
Proceedings of the 25th international conference on Machine learning
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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We introduce a new class of dynamic models for networks that extends stochastic blockmodels to settings where the interactions between a group of actors are observed at multiple points in time. Our goal is to identify structural changes in model features such as differential attachment, homophily by attributes, transitivity, and clustering as the network evolves. Our focus is on Bayesian inference, so the models are constructed hierarchically by combining different classes of Bayesian nonparametric priors. The methods are illustrated through a simulation study and two real data sets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 218–234, 2012 © 2012 Wiley Periodicals, Inc.