Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
Reasoning with BKBs – Algorithms and Complexity
Annals of Mathematics and Artificial Intelligence
The application of hidden Markov models in speech recognition
Foundations and Trends in Signal Processing
Anomaly pattern detection in categorical datasets
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On a framework for the prediction and explanation of changing opinions
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
PET: a statistical model for popular events tracking in social communities
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Fusing multiple Bayesian knowledge sources
International Journal of Approximate Reasoning
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An important task of modeling complex social behaviors is to observe and understand individual/group beliefs and attitudes. These beliefs, however, are not stable and may change multiple times as people gain additional information/perceptions from various external sources, which in turn, may affect their subsequent behavior. To detect and track such influential sources is challenging, as they are often invisible to the public due to a variety of reasons--private communications, what one randomly reads or hears, and implicit social hierarchies, to name a few. Existing approaches usually focus on detecting distribution variations in behavioral data, but overlook the underlying reason for the variation. In this paper, we present a novel approach that models the belief change over time caused by hidden sources, taking into consideration the evolution of their impact patterns. Specifically, a finite fusion model is defined to encode the latent parameters that characterize the distribution of the hidden sources and their impact weights. We compare our work with two general mixture models, namely Gaussian Mixture Model and Mixture Bayesian Network. Experiments on both synthetic data and a real-world scenario show that our approach is effective on detecting and tracking hidden sources and outperforms existing methods.