Nice! I am especially interested in Bayesian Networks, which are only one of the many models that can be expressed by a factor graph representation. Do you do Bayesian Networks learning at scale (parameters and structure) with latent variables? Are you using publicly available tools for that? Which ones?
LibDAI, which created the supported format, "supports parameter learning of conditional probability tables by Expectation Maximization" according to the documentation. Is it your reference tool? Bertrand On Thu, Dec 15, 2016 at 5:21 AM, Bryan Cutler <cutl...@gmail.com> wrote: > I'll check it out, thanks for sharing Alexander! > > On Dec 13, 2016 4:58 PM, "Ulanov, Alexander" <alexander.ula...@hpe.com> > wrote: > >> 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 >> >
