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.
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