https://dotnet.github.io/infer/
Infer.NET is a framework for running Bayesian inference in graphical models
https://dotnet.github.io/infer/
bayesian-inference machine-learning
Last synced: 8 months ago
JSON representation
Infer.NET is a framework for running Bayesian inference in graphical models
- Host: GitHub
- URL: https://dotnet.github.io/infer/
- Owner: dotnet
- License: mit
- Created: 2018-09-14T23:49:02.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2024-09-04T23:51:14.000Z (about 1 year ago)
- Last Synced: 2024-10-29T15:17:50.251Z (about 1 year ago)
- Topics: bayesian-inference, machine-learning
- Language: C#
- Homepage: https://dotnet.github.io/infer/
- Size: 34.5 MB
- Stars: 1,558
- Watchers: 102
- Forks: 228
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE-OF-CONDUCT.md
Awesome Lists containing this project
- awesome-machine-learning - Infer.NET - Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others. (.NET / [Tools](#tools-1))
- awesome-machine-learning - Infer.NET - Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others. (.NET)
- awesome-dot-dev - Infer.NET - A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. (Machine Learning and Data Science)
- awsome-dotnet - Infer.NET - A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. (Machine Learning and Data Science)
- awesome-csharp - Infer.NET - A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. (Machine Learning and Data Science)
- awesome-dotnet-cn - Infer.NET - 在绘图模型上运行Bayesian接口的框架,亦可用于概率编程。 (机器学习和数据科学)
- awesome-machine-learning - Infer.NET - Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others. (.NET / [Tools](#tools-1))
- awesome-dotnet - Infer.NET - A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. (Machine Learning and Data Science)
- awesome-dotnet - Infer.NET - A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. (Machine Learning and Data Science / GUI - other)
- awesome-machine-learning - Infer.NET - Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others. (.NET / [Tools](#tools-1))
- awesome-dotnet-datascience - Infer.NET - A framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming. (Machine Learning and Differential Programming)