The AMIDST Toolbox is an open source Java software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modelling language based on probabilistic graphical models with latent variables. AMIDST provides parallel and distributed implementations of scalable algorithms for doing probabilistic inference and Bayesian parameter learning in the specified models. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continuous variables from a wide range of probability distributions.
- Probabilistic graphical models
- Scalable algorithms
- Variational methods
- Latent variables
Masegosa, A., Martinez, A. M., Ramos-López, D., Cabanas de Paz, R., Salmerón, A., Langseth, H., Nielsen, T. D., & Madsen, A. L. (2019). AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems, 163, 595-597. https://doi.org/10.1016/j.knosys.2018.09.019