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Flexible structure learning under uncertainty [PDF]
Frontiers in Neuroscience, 2023 Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments.
Rui Wang +5 more
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Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning [PDF]
Sensors, 2023 Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant ...
Alex Thomas +2 more
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Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities [PDF]
EntropyThe causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables.
Daniel Chicharro, Julia K. Nguyen
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Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks [PDF]
Entropy, 2022 Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between
Xiaohan Wei, Yulai Zhang, Cheng Wang
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Improved Local Search with Momentum for Bayesian Networks Structure Learning
Entropy, 2021 Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex ...
Xiaohan Liu +3 more
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Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network
IEEE Access, 2021 Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases.
Luxuan Qu +6 more
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Entropy, 2020 Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning.
Zhigao Guo, Anthony C. Constantinou
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The variance of causal effect estimators for binary v-structures
Journal of Causal Inference, 2022 Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
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IEEE Access, 2021 At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to ...
Kun Liu, Yani Cui, Jia Ren, Peiran Li
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A Novel BN Learning Algorithm Based on Block Learning Strategy
Sensors, 2020 Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and ...
Xinyu Li, Xiaoguang Gao, Chenfeng Wang
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