Amidst - A Java Toolbox for Analytics of Massive Data Streams using Probabilistic Graphical Models

A Java Toolbox for Scalable Probabilistic Machine Learning. Model your problem using a flexible probabilistic language based on graphical models. Then, fit it with data using a Bayesian approach to handle modelling uncertainty. Multi-core and distributed processing. AMIDST provides tailored parallel and distributed implementations of Bayesian parameter learning and probabilistic inference for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. Data .

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Cajamar Data Lab

Los proyectos Big Data son uno de los núcleos de la estrategia de innovación del Grupo Cooperativo Cajamar. Los mejores trabajos de la PythonHack 2016. Es la competición online de analítica de datos en base a datos bancarios reales del Grupo Cajamar organizada con motivo de la PyConES 2016. La conferencia nacional de Python más importante de España. La Pita y el Lagarto. La Pita y el Lagarto. Accesit IBM a la mejor StartUp.

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Amidst - A Java Toolbox for Analytics of Massive Data Streams using Probabilistic Graphical Models

DESCRIPTION

A Java Toolbox for Scalable Probabilistic Machine Learning. Model your problem using a flexible probabilistic language based on graphical models. Then, fit it with data using a Bayesian approach to handle modelling uncertainty. Multi-core and distributed processing. AMIDST provides tailored parallel and distributed implementations of Bayesian parameter learning and probabilistic inference for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. Data .

PARSED CONTENT

The web site states the following, "A Java Toolbox for Scalable Probabilistic Machine Learning." We noticed that the web site stated " Model your problem using a flexible probabilistic language based on graphical models." It also said " Then, fit it with data using a Bayesian approach to handle modelling uncertainty. AMIDST provides tailored parallel and distributed implementations of Bayesian parameter learning and probabilistic inference for batch and streaming data. This processing is based on flexible and scalable message passing algorithms."

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