Results 51 to 60 of about 2,475,679 (305)
Empirical Bayesian learning in AR graphical models [PDF]
Automatica, 2019 Automatica (accepted)
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Bayesian Quantum Neural Networks
IEEE Access, 2022 The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine ...
Nam Nguyen, Kwang-Cheng Chen
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Hyperparameter Estimation for Sparse Bayesian Learning Models
SIAM/ASA Journal on Uncertainty QuantificationSparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL models are crucial for the model's performance, but they are often difficult to estimate due to the non-convexity and the high-dimensionality of the associated objective ...
Yu, Feng, Shen, Lixin, Song, Guohui
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Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas +7 more
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A Bayesian Learning Method for Financial Time-Series Analysis
IEEE Access, 2018 This article develops a sequential Bayesian learning method to estimate the parameters and recover the state variables for generalized autoregressive conditional heteroscedasticity (GARCH) models, which are commonly used in the financial time-series ...
Fumin Zhu +3 more
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Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito +14 more
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Arthritis Care &Research, EarlyView.Objective In complex diseases, it is challenging to assess a patient's disease state, trajectory, treatment exposures, and risk of multiple outcomes simultaneously, efficiently, and at the point of care. Methods We developed an interactive patient‐level data visualization and analysis tool (VAT) that automates illustration of the trajectory of a ...
Ji Soo Kim +18 more
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Application of Improved LightGBM Model in Blood Glucose Prediction
Applied Sciences, 2020 In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medical ...
Yan Wang, Tao Wang
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Singular Model and Bayesian Learning.
The Brain & Neural Networks, 2003 人工的神経回路網に代表される多くの確率的推論モデルは, フィッシャー情報行列が特異となるパラメータを持つため, 統計的正則モデルではないことが知られている. 現在でも, その学習の数学的な性質の多くは謎のままであるが, 近年の研究の進展により, ベイズ学習については多くの事実が解明されるようになってきた. 本論では, 特異モデルにおけるベイズ学習について, 数学的な美しさと実世界問題における有用性を解説する.
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
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