Suchergebnisse - Learning and Inference in Bayesian Networks

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation von Liu, Jiaming, Zhang, Xuemei, Xiong, Haitao

“… —data processing, structural learning, parameter learning, and interpretation of inferences …”
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A review on evolutionary algorithms in Bayesian network learning and inference tasks von Larrañaga, Pedro, Karshenas, Hossein, Bielza, Concha, Santana, Roberto

“… Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms …”
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Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference von Chua, Alvin J. K., Vallisneri, Michele

“… We seek to achieve the holy grail of Bayesian inference for gravitational-wave astronomy …”
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Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo von Yu, Kaixian, Cui, Zihan, Sui, Xin, Qiu, Xing, Zhang, Jinfeng

“… Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures …”
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Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey von Shiguihara, Pedro, Lopes, Alneu De Andrade, Mauricio, David

“… Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference …”
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CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data von McGeachie, Michael J., Chang, Hsun-Hsien, Weiss, Scott T.

“… CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs …”
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Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks von Paluszewski, Martin, Hamelryck, Thomas

“… Background Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs …”
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Dynamic Knowledge Inference Based on Bayesian Network Learning von Wang, Deyan, Xiao, Ying, AmrilJaharadak, Adam

“… On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis …”
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Causal inference and Bayesian network structure learning from nominal data von Luo, Guiming, Zhao, Boxu, Du, Shiyuan

“… This article also proposes a Bayesian network construction algorithm based on discrete causal inference (BDCI …”
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Lifelong Incremental Reinforcement Learning With Online Bayesian Inference von Wang, Zhi, Chen, Chunlin, Dong, Daoyi

“… A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes and to incrementally …”
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Hierarchical Bayesian Inference and Learning in Spiking Neural Networks von Guo, Shangqi, Yu, Zhaofei, Deng, Fei, Hu, Xiaolin, Chen, Feng

“… In this paper, we propose a hierarchical network of winner-take-all circuits which can carry out hierarchical Bayesian inference and learning through a spike-based variational expectation maximization (EM) algorithm …”
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Ontology-Driven Learning of Bayesian Network for Causal Inference and Quality Assurance in Additive Manufacturing von Chen, Ruimin, Lu, Yan, Witherell, Paul, Simpson, Timothy W., Kumara, Soundar, Yang, Hui

“… In this paper, we develop an ontology-based Bayesian network (BN) model to represent causal relationships between AM parameters (i.e …”
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Optimizing inventory management: a causal inference-driven Bayesian network with transfer learning adaptation von Zhu Xi, Wei Guan, Ahmet Savasan

“… To enhance inventory forecasting and optimization in supply chain management, this study proposes a Transfer-learning Bayesian Network (TBN …”
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Sparse Bayesian Learning for Sequential Inference of Network Connectivity From Small Data von Wan, Jinming, Kataoka, Jun, Sivakumar, Jayanth, Pena, Eric, Che, Yiming, Sayama, Hiroki, Cheng, Changqing

“… In this work, we propose a novel Bayesian sequential learning approach to reconstruct network connectivity adaptively …”
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Bayesian Inference and Online Learning in Poisson Neuronal Networks von Huang, Yanping, Rao, Rajesh P N

“… Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning …”
Weitere Angaben

A Review on Causal Inference Models Combining Bayesian Networks with Deep Learning in Environmental Health Research von Susymary Johnson

“… This literature review explores the application of causal inference models, specifically Bayesian networks (BN …”
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Learning Bayesian networks with low inference complexity von Benjumeda, Marco, Larrañaga, Pedro, Bielza, Concha

“… inefficient inference models. In this paper we propose a framework for learning low inference complexity Bayesian networks …”
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CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data: e1003676 von McGeachie, Michael J, Chang, Hsun-Hsien, Weiss, Scott T

“… CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs …”
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Federated learning meets Bayesian neural network: Robust and uncertainty-aware distributed variational inference von Li, Pengfei, Hu, Qinghua, Wang, Xiaofei

“… To tackle the aforementioned problems, we introduce a novel approach explicitly integrating Bayesian neural networks (BNNs …”
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Decentralized Black-Box Variational Inference for Bayesian Learning on Sensor Networks von Cadena, Jose, Ray, Priyadip, Goldhahn, Ryan

“… Fully decentralized Bayesian inference in large autonomous sensor networks remains a challenging research problem, due to the inherent latency of Markov Chain Monte Carlo (MCMC) inference …”
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