JuliaCon 2022 (Times are UTC)

Martin Roa-Villescas

Martin Roa-Villescas received his B.Sc. degree in Electronic Engineering from the National University of Colombia, Manizales, Colombia in 2010, and his M.Sc. degree in Embedded Systems from the Eindhoven University of Technology (TU/e), Eindhoven, The Netherlands, in 2013. He is currently pursuing a Ph.D. degree in Bayesian Machine Learning with a special track in education at TU/e. From 2013 to 2018, he worked as an embedded software designer in Philips Research, Eindhoven, The Netherlands. His research interests include probabilistic graphical models, probabilistic programming, and embedded systems.


JunctionTrees: Bayesian inference in discrete graphical models
Martin Roa-Villescas

JunctionTrees.jl implements the junction tree algorithm: an efficient method to perform Bayesian inference in discrete probabilistic graphical models. It exploits Julia's metaprogramming capabilities to separate the algorithm into a compilation and a runtime phase. This opens a wide range of optimization possibilities in the compilation stage. The non-optimized runtime performance of JunctionTrees.jl is similar to those of analog C++ libraries such as libdai and Merlin.