Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nir Friedman, Daphne Koller
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Efficacy of Inebilizumab in N‐MOmentum Trial Participants With or Without Prior Immunosuppressants
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT This post hoc analysis examined the impact of prior immunosuppressants on the long‐term efficacy and safety of inebilizumab, a cluster of differentiation 19+ B‐cell–depleting monoclonal antibody, in participants with aquaporin‐4–seropositive neuromyelitis optica spectrum disorder from the N‐MOmentum trial (NTC02200770).
Bruce A. C. Cree +9 more
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Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints [PDF]
, 2012 Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including probabilistic ...
Anandkumar, Animashree +3 more
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Clinical, histological, and serological predictors of renal function loss in lupus nephritis.
Arthritis Care &Research, Accepted Article.Objective Kidney survival is the ultimate goal in lupus nephritis (LN) management, but long‐term predictors remain inadequately studied, requiring long‐term follow‐up. This study aimed to identify baseline and early longitudinal predictors of kidney survival in the Accelerating Medicines Partnership LN longitudinal cohort.
Shangzhu Zhang +21 more
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Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
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Improving the performance of Bayesian networks in non-ignorable missing data imputation
Kuwait Journal of Science, 2013 The issue of missing data may arise for researchers who deal with data gathering problems. Bayesian networks are one of the proposed methods that have been recently used in missing data imputation.
P. NILOOFAR +2 more
doaj
Bayesian network–response regression [PDF]
Bioinformatics, 2017 Abstract Motivation There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited.
Lu Wang 0015 +3 more
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
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Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity
Advanced Functional Materials, EarlyView.Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen +11 more
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