JunctionTrees.jl - Efficient Bayesian Inference in Discrete Graphical Models
Jul 27, 2022·
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0 min read
Martin Roa Villescas
Image credit: UnsplashAbstract
‘JunctionTrees.jl encapsulates the result of the research we have been conducting in the context of improving the efficiency of Bayesian inference in probabilistic graphical models. The junction tree algorithm is a core component of discrete inference in probabilistic graphical models. It lies at the heart of many courses that are taught at different universities around the world including MIT, Berkeley, and Stanford. Moreover, it serves as the backbone of successful commercial software, such as Hugin Expert, that aims to discover insight and provide predictive capabilities to effectively combat fraud and risk. JunctionTrees.jl is mainly tailored towards students and researchers. This library offers a great starting point for understanding the implementation details of this algorithm thanks to the intrinsic readability of the Julia language and the thoroughly commented codebase. Moreover, this package constitutes an optimization framework that other researchers can make use of to experiment with different ideas to improve the performance of runtime Bayesian inference.’
Date
Jul 27, 2022 2:30 PM — 2:40 PM
Event
Authors
Teacher & Researcher
Martin Roa Villescas holds a BSc in Electronic Engineering from the National
University of Colombia and an MSc in Embedded Systems from Eindhoven
University of Technology (TU/e). He worked at Philips Research as an
embedded software designer from 2013 to 2018. He later returned to TU/e for
his doctoral research in model-based machine learning, carried out within
the PhD-Teaching Assistant trajectory combining research and teaching. Since
2023, he has been working at Fontys University of Applied Sciences in the
Netherlands, where he teaches in the Information and Communication
Technology program and conducts research in robotics and smart industry.