Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Intrusion detection using continuous time Bayesian networks
Journal of Artificial Intelligence Research
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
The Journal of Machine Learning Research
Continuous time Bayesian network classifiers
Journal of Biomedical Informatics
Multi-label relational neighbor classification using social context features
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of the model than the standard method of moments algorithm from the sociology literature. We extend the existing social network models to allow for indirect and asynchronous observations of the links. A Markov chain Monte Carlo sampling algorithm for this new model permits estimation and inference. We provide results on both a synthetic network (for verification) and real social network data.