Results 51 to 60 of about 85,238 (262)

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, M. GANJALI, AND M.R. FARID ROHANI   +2 more
doaj  

Global Rather Than Vertical‐Selective Saccadic Abnormalities in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To test whether vertical saccades are preferentially affected in Progressive Supranuclear Palsy (PSP). Methods PSP patients (n = 24) were compared to age‐matched controls (n = 94) and two degenerative groups (Alzheimer's disease, n = 20; Lewy body disease, n = 50).
Duy Duan Nguyen, Mark Paine, Soohyun Lee, Amir Fazlollahi, Baibin (Ben) Wang, John D. O'Sullivan, Peter J. Nestor   +6 more
wiley   +1 more source

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, Chang Chen, Lu Wang, Hongwei Kang, Yong Shen, Qingyi Chen   +5 more
doaj   +1 more source

Integrating Boolean Satisfiability Algorithms into Bayesian Networks for Accelerated Deterministic Inference

Algorithms
Exact probabilistic inference in Bayesian Networks (BNs) becomes increasingly expensive as network size and structural complexity grow, limiting its applicability in time-sensitive decision-support systems.
Efraín Evaristo Díaz Macías, José Steven Cordero Bazurto, Byron Wladimir Oviedo Bayas   +2 more
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga   +2 more
wiley   +1 more source

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, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Minimax Bayesian Neural Networks

Entropy
Robustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field.
Junping Hong, Ercan Engin Kuruoglu
doaj   +1 more source

Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution

International Journal of Mathematics and Mathematical Sciences, 2011
Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade ...
Linda Smail
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Relational Bayesian Networks

CoRR, 1997
A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network.
openaire   +4 more sources