Abstract
Variational Bayesian (VB) inference has become an increasingly popular method for approximating exact Bayesian inference in model-based machine learning. The VB approach provides a way to trade off accuracy versus computational complexity and scales better to large-dimensional inference problems than sampling solutions. The Julia package ReactiveMP.jl implements and automates reactive VB inference by minimization of a constrained Bethe Free Energy functional through message passing on a factor graph representation of a probabilistic model. Moreover, through support for specification of explicit constraints on the Free Energy functional, ReactiveMP.jl allows for comparative analysis of different variational cost function proposals.
Bart van Erp
Chief Product Officer LazyDynamics
Bart van Erp is co-founder & product lead of Lazy Dynamics.
Albert Podusenko
Chief Executive Officer LazyDynamics
Albert Podusenko is a founder & CEO of Lazy Dynamics.
