2023
DOI: 10.21105/joss.05161
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RxInfer: A Julia package for reactive real-time Bayesian inference

Abstract: Bayesian inference realizes optimal information processing through a full commitment to reasoning by probability theory. The Bayesian framework is positioned at the core of modern AI technology for applications such as speech and image recognition and generation, medical analysis, robot navigation, and more. The framework describes how a rational agent should update its beliefs when new information is revealed by the agent's environment. Unfortunately, perfect Bayesian reasoning is generally intractable, since… Show more

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Cited by 10 publications
(4 citation statements)
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“…All experiments were performed using the scientific programming language Julia [ 48 ] with the state-of-the-art probabilistic programming package RxInfer.jl [ 9 ]. The mixture node specified in Section 4.2 was integrated in its message-passing engine ReactiveMP.jl [ 49 , 50 ].…”
Section: Methodsmentioning
confidence: 99%
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“…All experiments were performed using the scientific programming language Julia [ 48 ] with the state-of-the-art probabilistic programming package RxInfer.jl [ 9 ]. The mixture node specified in Section 4.2 was integrated in its message-passing engine ReactiveMP.jl [ 49 , 50 ].…”
Section: Methodsmentioning
confidence: 99%
“…Automating the model design cycle [ 2 ] under the Bayesian formalism has been the goal of many probabilistic programming languages [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. This paper focuses on message passing-based approaches, which leverage the conditional independencies in the model structure for performing probabilistic inference, e.g., [ 27 , 28 , 29 , 30 ], which will be formally introduced in Section 3.2 .…”
Section: Related Workmentioning
confidence: 99%
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