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Additive Bayesian Networks

Journal of Open Source Software
The R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model. BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data.
Delucchi, Matteo, Liechti, Jonas I, Spinner, Georg R, Furrer, Reinhard   +3 more
openaire   +2 more sources

GENERATIONS IN BAYESIAN NETWORKS

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019
This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents belonging to previous generations. Terminology for this concept, in our opinion, has not yet fully developed.
Alexander Litvinenko, Natalya Litvinenko, Orken Mamyrbayev, Assem Shayakhmetova   +3 more
openaire   +3 more sources

Machine Learning‐Enabled Drug‐Induced Toxicity Prediction

Advanced Science, EarlyView.
Unexpected toxicity accounts for 30% of drug development failures. This review highlights ML innovations in predicting drug‐induced toxicity, emphasizing comparative analyses, interpretable algorithms, and multi‐source data integration. It categorizes toxicity types, summarizes ML models, and organizes key databases, offering strategies to address ...
Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo   +5 more
wiley   +1 more source

HyPE: Online Hybrid Pseudo-Bayesian Estimation Method for S-ALOHA-Based Tactical FANETs

IEEE Access
Significant challenges are involved in tactical flying ad-hoc network (FANET) missions because network environments are very dynamic. In addition, energy-efficient network operation is important in tactical FANETs owing to the limited capacity of the on ...
Jimin Jeon, Junseung Lee, Taewook Kim, Jaeha Ahn, Youngbin Yu, Min Lee, Heejung Yu, Howon Lee   +7 more
doaj   +1 more source

Hardware implementation of Bayesian network building blocks with stochastic spintronic devices [PDF]

arXiv, 2020
Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For systems that involve complex causal dependencies among many variables, the complexity of the associated Bayesian networks become computationally intractable.
arxiv  

Who's Afraid of Thomas Bayes? [PDF]

arXiv, 2021
In many cases, neural networks perform well on test data, but tend to overestimate their confidence on out-of-distribution data. This has led to adoption of Bayesian neural networks, which better capture uncertainty and therefore more accurately reflect the model's confidence.
arxiv  

Uncover Hidden Physical Information of Soft Matter by Observing Large Deformation

Advanced Science, EarlyView.
Detecting internal abnormalities in soft matter remains challenging due to its heterogeneous nature. This study introduces a method that infers hidden physical properties by matching observed deformation with simulation through parallel Bayesian optimization.
Huanyu Yang, Yitao Cheng, Penghui Zhao, Jiageng Cai, Zhaowei Yin, Shaomin Chen, Ge Guo, Chi Zhu, Ke Liu, Lingyun Zu   +9 more
wiley   +1 more source

Bayesian Model Averaging Using the k-best Bayesian Network Structures [PDF]

arXiv, 2012
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks.
arxiv  

Hierarchical Regulatory Networks Reveal Conserved Drivers of Plant Drought Response at the Cell‐Type Level

Advanced Science, EarlyView.
This study presents a comprehensive multi‐omics framework to uncover cell‐type‐specific regulatory networks in plant drought responses. By integrating transcriptomic, proteomic, and epigenetic data from nearly 30 000 samples, key regulators such as CIPK23 and NLP7 are identified, revealing insights into conserved drought tolerance mechanisms and ...
Moyang Liu, Yuanyuan Xu, Yue Song, Dongying Fan, Junpeng Li, Zhen Zhang, Lujia Wang, Juan He, Cheng Chen, Chao Ma   +9 more
wiley   +1 more source

The Computational Power of Dynamic Bayesian Networks [PDF]

Proceedings of the 4th International Workshop on Artificial Intelligence and Cognition co-located with the Joint Multi-Conference on Human-Level Artificial Intelligence (HLAI 2016) 158-166, 2016
This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-complete computation.
arxiv