# AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning

arXiv: https://arxiv.org/abs/1704.01427 · HTML: https://mlsanity.com/paper/1704.01427/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models](https://mlsanity.com/paper/2402.15075/) (2024)

## Abstract (arXiv)

The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial 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 implementa- tions 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 continu- ous 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 version 2.0.