We introduce Reactive Message Passing (RMP) as a framework for executing schedule-free, robust and scalable message passing-based inference in a factor graph representation of a probabilistic model. RMP is based on the reactive programming style that only describes how nodes in a factor graph react to changes in connected nodes. The absence of a fixed message passing schedule improves robustness, scalability and execution time of the inference procedure. We also present ReactiveMP.jl, which is a Julia package for realizing RMP through minimization of a constrained Bethe free energy. By user-defined specification of local form and factorization constraints on the variational posterior distribution, ReactiveMP.jl executes hybrid message passing algorithms including belief propagation, variational message passing, expectation propagation, and expectation maximisation update rules. Experimental results demonstrate the improved performance of ReactiveMP-based RMP in comparison to other Julia packages for Bayesian inference across a range of probabilistic models. In particular, we show that the RMP framework is able to run Bayesian inference for large-scale probabilistic state space models with hundreds of thousands of random variables on a standard laptop computer.
翻译:我们引入了Reactive Missing (RMP),作为在概率模型的系数图表中执行无计划、稳健和可缩放信息传动推导的框架。RMP 是基于反应性编程风格的,它只描述一个系数图中的节点如何对连接节点的变化作出反应。没有固定的电文传递时间表可以提高导引程序的稳健性、可缩放性和执行时间。我们还介绍了ReactiveMP.jl, 这是一个Julia的套件,通过最大限度地减少受限的Bethe自由能量实现RMP。通过用户定义的关于变异后传分布的本地形式和因子化限制的规格,ReactiveMP.jl 执行混合电文传递算法,包括信仰传播、变异信息传递、预期传播和预期最大化更新规则。实验结果显示,与巴伊西亚其他的Julia 组合相比,在一系列概率模型中,雷命MP jl 。我们特别表明, RMP 框架能够对大规模计算机移动移动模型进行Bayesian 的随机推判, 数百个标准空间状态模型。