Dear Spark developers and users,
HPE has open sourced the implementation of the belief propagation (BP) algorithm for Apache Spark, a popular message passing algorithm for performing inference in probabilistic graphical models. It provides exact inference for graphical models without loops. While inference for graphical models with loops is approximate, in practice it is shown to work well. The implementation is generic and operates on factor graph representation of graphical models. It handles factors of any order, and variable domains of any size. It is implemented with Apache Spark GraphX, and thus can scale to large scale models. Further, it supports computations in log scale for numerical stability. Large scale applications of BP include fraud detection in banking transactions and malicious site detection in computer networks. Source code: https://github.com/HewlettPackard/sandpiper Best regards, Alexander
