Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is currently downloadable as a beta release under a non-commercial license.
For more information about Infer.NET including documentation and examples please see the main Infer.NET page.
- Nevena Lazic, C. M. Bishop, and J. Winn, Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables, in Proceedings Sixteenth International Conference on Artificial Intelligence and Statistics (AIStats), AISTATS, 2013.
- John Winn, Causality with Gates, in Proceedings Artificial Intelligence and Statistics, The Society for Artificial Intelligence and Statistics, April 2012.
- Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, and Zheng Chen, A Novel Click Model and Its Applications to Online Advertising, in Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010), Association for Computing Machinery, Inc., 5 February 2010.
- Peter A. Flach, John Guiver, Mohammed J. Zaki, Sebastian Spiegler, Bruno Golenia, Ralf Herbrich, Thore Graepel, and Simon Price, Novel Tools To Streamline the Conference Review Process: Experiences from SIGKDD'09, in SIGKDD Explorations, vol. 11, no. (2), Association for Computing Machinery, Inc., December 2009.
- Tom Minka and John Winn, Gates, in Advances in Neural Information Processing Systems 21, 2009.
- Iain Buchan, John Winn, and Christopher Bishop, A Unified Modeling Approach to Data-Intensive Healthcare, in The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, 2009.
- Tom Minka and John Winn, Gates: A graphical notation for mixture models, no. MSR-TR-2008-185, 5 December 2008.