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A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.
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libDAI provides FOSS implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.
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JProGraM is an open-source Java library which can be used for learning the following probabilistic models from data: Bayesian networks, Markov random fields, hybrid random fields, probabilistic [...]
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The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms focusing on heterogeneous data integration and efficiency for large biological data collections.
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Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations.
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A C++ library of machine learning algorithms and tools, and several demos that show how to use it.
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