WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
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Variational methods for the Dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Latent Dirichlet Co-Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mixtures of hierarchical topics with Pachinko allocation
Proceedings of the 24th international conference on Machine learning
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
hArtes design flow for heterogeneous platforms
ISQED '09 Proceedings of the 2009 10th International Symposium on Quality of Electronic Design
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Typology of mixed-membership models: towards a design method
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Aspect extraction through semi-supervised modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Discovering coherent topics using general knowledge
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Leveraging multi-domain prior knowledge in topic models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a representation of topic models as "mixture networks", a domain-specific compact alternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of this representation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation and implementation of a generic Gibbs sampling algorithm.