ReactiveMP。jl:一个Julia包,用于基于响应消息传递的贝叶斯推理

Dmitry V. Bagaev, B. Vries
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引用次数: 2

摘要

ReactiveMP。jl是一个原生的Julia实现,在带有因子图的概率图形模型中实现基于响应消息传递的贝叶斯推理。该包做约束贝叶斯自由能量最小化,并支持精确和变分贝叶斯推理,为模型规范提供方便的语法,并允许额外的因式分解和形式约束规范的变分家族的分布。此外,ReactiveMP。Jl包含大量的标准概率模型,可以很容易地扩展到自定义的新节点和消息更新规则。与非反应性(命令式编码)贝叶斯推理包相比,ReactiveMP。Jl很容易扩展到支持在标准笔记本电脑上对具有数万个变量和数百万个节点的大型共轭模型进行推理。
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ReactiveMP.jl: A Julia Package for Reactive Message Passing-based Bayesian Inference
ReactiveMP.jl is a native Julia implementation of reactive message passing-based Bayesian inference in probabilistic graphical models with Factor Graphs. The package does Constrained Bethe Free Energy minimisation and supports both exact and variational Bayesian inference, provides a convenient syntax for model specification and allows for extra factorisation and form constraints specification of the variational family of distributions. In addition, ReactiveMP.jl includes a large range of standard probabilistic models and can easily be extended to custom novel nodes and message update rules. In contrast to non-reactive (imperatively coded) Bayesian inference packages, ReactiveMP.jl scales easily to support inference on a standard laptop for large conjugate models with tens of thousands of variables and millions of nodes.
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