Infer.NET is a .NET framework for machine learning. It provides state-of-the-art message-passing algorithms and statistical routines for performing Bayesian inference. It has applications in a wide variety of domains, including information retrieval, bioinformatics, epidemiology, vision, and many others.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
- Su, Hao; Yu, Adams Wei: Probabilistic modeling of scenes using object frames (2015)
- Kim, Sungchul; Qin, Tao; Liu, Tie-Yan; Yu, Hwanjo: Advertiser-centric approach to understand user click behavior in sponsored search (2014)
- Parson, Oliver; Ghosh, Siddhartha; Weal, Mark; Rogers, Alex: An unsupervised training method for non-intrusive appliance load monitoring (2014)
- Bishop, Christopher M.: Model-based machine learning (2013)
- Borgström, Johannes; Gordon, Andrew D.; Greenberg, Michael; Margetson, James; Van Gael, Jurgen: Measure transformer semantics for Bayesian machine learning (2013)
- Delatola, Eleni-Ioanna; Griffin, Jim E.: Bayesian nonparametric modelling of the return distribution with stochastic volatility (2011)
- Hall, Peter; Pham, Tung; Wand, M.P.; Wang, S.S.J.: Asymptotic normality and valid inference for Gaussian variational approximation (2011)
- Ormerod, John T.: Grid based variational approximations (2011)
- Wand, M.P.; Ormerod, J.T.: Penalized wavelets: embedding wavelets into semiparametric regression (2011)
- Wang, S.S.J.; Wand, M.P.: Using Infer.NET for statistical analyses (2011)