Results 31 to 40 of about 21,280,601 (295)
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 survey of Bayesian Network structure learning [PDF]
Artificial Intelligence Review, 2021 Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-world
N. K. Kitson +4 more
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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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The impact of prior knowledge on causal structure learning [PDF]
Knowledge and Information Systems, 2021 Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions.
A. Constantinou +2 more
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Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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Temporal context and latent state inference in the hippocampal splitter signal
eLife, 2023 The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central
Éléonore Duvelle +2 more
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Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification [PDF]
AAAI Conference on Artificial Intelligence, 2021 Recently, graph neural networks (GNNs) have been widely used for document classification. However, most existing methods are based on static word co-occurrence graphs without sentence-level information, which poses three challenges:(1) word ambiguity, (2)
Yinhua Piao +3 more
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Multiscale Causal Structure Learning
, 2022 The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE
Gabriele D'Acunto +2 more
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Synthesis of a novel photoactivatable glucosylceramide cross-linker
Journal of Lipid Research, 2016 The biosynthesis of glucosylceramide (GlcCer) is a key rate-limiting step in complex glycosphingolipid (GSL) biosynthesis. To further define interacting partners of GlcCer, we have made a cleavable, biotinylated, photoreactive GlcCer analog in which the ...
Monique Budani +3 more
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