Results 41 to 50 of about 394,931 (262)

Additive Bayesian Networks

Journal of Open Source Software
The R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model. BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data.
Delucchi, Matteo, Liechti, Jonas I, Spinner, Georg R, Furrer, Reinhard   +3 more
openaire   +2 more sources

The temporal trends and short‐ and long‐term mortality of people with acute myocardial infarction and rheumatoid arthritis: a nationwide cohort study

Arthritis Care &Research, Accepted Article.
Aims We investigated whether a diagnosis of rheumatoid arthritis (RA) affects the quality of inpatient acute myocardial infarction (AMI) care and long‐term mortality post‐AMI. Methods We analysed data from 784,091 adults, 6,047 with a diagnosis of RA, from England and Wales hospitalised with AMI between 2005 and 2019 from the MINAP registry, linked ...
Megan Butler, Nicholas Weight, Muhammad Rashid, Rodrigo Bagur, Purvi Parwani, Samantha Hider, Edward Roddy, Robert Butler, Mamas A Mamas   +8 more
wiley   +1 more source

Addressing Economic Insecurities Can Improve Patient‐Reported Outcomes in Lupus

Arthritis Care &Research, Accepted Article.
Background Economic insecurities, such as food, housing, transportation, and financial challenges, are modifiable risk factors and influence patient‐reported outcomes (PROs) in systemic lupus erythematosus (SLE). We examined: 1) associations between economic insecurities and PROs; 2) the impact of screening and addressing economic insecurities during ...
Jay Patel, Tripti Singh, Meredith Ingersoll, Shelby Gomez, Amanda Weber, Sarah E. Panzer, Sancia Ferguson, Christie M. Bartels, Shivani Garg   +8 more
wiley   +1 more source

Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks

Frontiers in Artificial Intelligence
Previous work has demonstrated that initialization is very important for both fitting a neural network by gradient descent methods, as well as for Variational inference of Bayesian neural networks.
Devesh Jawla, John Kelleher
doaj   +1 more source

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

In Materia Shaping of Randomness with a Standard Complementary Metal‐Oxide‐Semiconductor Transistor for Task‐Adaptive Entropy Generation

Advanced Functional Materials, EarlyView.
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak, Ryun‐Han Koo, Youngchan Cho, Changhyeon Han, Dongbin Kim, Soi Jeong, Yunho Shin, Joonhyeok Choi, Jiseong Im, Jonghyun Ko, Jong‐Ho Lee, Jangsaeng Kim, Youngho Kang, Wonjun Shin, Daewoong Kwon   +14 more
wiley   +1 more source

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
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

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, Hsu, Daniel, Javanmard, Adel, Kakade, Sham M.   +3 more
core   +2 more sources

Accelerated Discovery of High Performance Ni3S4/Ni3Mo HER Catalysts via Bayesian Optimization

Advanced Functional Materials, EarlyView.
Integrated workflow accelerates the catalyst discovery of hydrogen evolution reaction via Bayesian optimization. An experiment‐trained surrogate model proposes synthesis conditions, guiding iterative refinement using electrochemical performance metrics.
Namuersaihan Namuersaihan, Zhiqiang Zhao, Oliver J. Conquest, Ying Shu, Haoyue Sun, Chunjing Su, Qi Cheng, Aloysius Soon, Catherine Stampfl, Jun Huang   +9 more
wiley   +1 more source