Scaling Probabilistic Inference Through Message Contraction Optimization-Reference-Cited by-同舟云学术

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Scaling Probabilistic Inference Through Message Contraction Optimization

Published:2023-07-24 Issue: Volume: Page:
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Container-title:2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)
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Author:

Villescas Martin Roa¹,Liu Jin-Guo²,Wijnings Patrick W.A.¹,Stuijk Sander¹,Corporaal Henk¹

Affiliation:

1. Eindhoven University of Technology,Eindhoven,The Netherlands

2. Hong Kong University of Science and Technology (Guangzhou),Guangzhou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10487132/10487174/10487633.pdf?arnumber=10487633

Reference15 articles.

1. The computational complexity of probabilistic inference using Bayesian belief networks;Cooper,1990

2. Finding maps for belief networks is np-hard;Shimony,1994

3. Approximating probabilistic inference in bayesian belief networks is np-hard;Dagum,1993

4. Approximating maps for belief networks is np-hard and other theorems;Abdelbar,1998

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On the Importance of the Execution Schedule for Bayesian Inference;ACM Transactions on Probabilistic Machine Learning;2024-08-31

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