Results 61 to 70 of about 3,102,730 (292)

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  

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

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, Marco Zaffalon   +3 more
wiley   +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

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

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Bayesian Learning of Markov Network Structure [PDF]

, 2006
We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina, Aleks Jakulin, Jakulin, Aleks, Irina Rish   +3 more
core   +2 more sources

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +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