InferPy is a Python package for probabilistic modeling with deep neural networks. InferPy defines a user-friendly API which trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, Inferpy allows to define, learn and evaluate general hierarchical probabilistic models containing deep neural networks in a compact and simple way. InferPy is built on top of Tensorflow, Edward2 and Keras.
Cabañas, R., Salmerón, A., & Masegosa, A. R. (2019). InferPy: Probabilistic modeling with Tensorflow made easy. Knowledge-Based Systems, 168, 25-27.
Cozar J., Cabañas, R., Salmerón, A., & Masegosa, A. R. (2020). InferPy: Deep Probabilistic modeling with Tensorflow made easy. Accepted for publication in Neurocomputing.
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a special
focus on (massive) streaming data. The toolbox supports a flexible modeling language
based on probabilistic graphical models with latent variables and temporal dependencies. The
specified models can be learnt from large data sets using parallel or distributed implementations
of Bayesian learning algorithms for either streaming or batch data. These algorithms are
based on a flexible variational message passing scheme, which supports discrete and continuous
variables from a wide range of probability distributions. AMIDST also leverages existing
functionality and algorithms by interfacing to software tools such as Flink, Spark, MOA,
Weka, R and HUGIN. AMIDST is an open source toolbox written in Java and available at
http://www.amidsttoolbox.com under the Apache Software License 2.0.
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas,
Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen (2019)
AMIDST: a Java toolbox for scalable probabilistic machine learning.
Knowledge Based Systems 163, 595-597.
The Elvira system is a Java tool to construct probabilistic models-driven based decision support systems. Elvira works with Bayesian networks and influence diagrams and it can operate with discrete, continuous and temporal variables. It has an easy to use Graphical User Interface (GUI).