Results 91 to 100 of about 7,749,372 (301)

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm [PDF]

, 2013
We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data.
Cooke, Emma J., Wild, David L., Robert Darkins, Emma J Cooke, Darkins Robert, Cooke Emma J., Ghahramani Zoubin, Savage, Richard S., Paul D W Kirk, David L Wild, Ghahramani, Zoubin, Kirk Paul D. W., Zoubin Ghahramani, Richard S Savage, Darkins, Robert, Savage Richard S., Kirk, P. (Paul), Wild David L.   +17 more
core   +1 more source

Robotics in Ionizing Radiation Environments: Vulnerabilities, Hardening, and Future Directions

Advanced Robotics Research, EarlyView.
Radiation exposure causes cumulative degradation in robotic sensors, actuators, and electronic logic, often leading to undetected failures. This study maps component vulnerabilities, evaluates hardening and shielding strategies, and identifies testing gaps that must be addressed to enable next‐generation radiation‐tolerant robotic systems.
Francis A. K. Djirackor, Josephine T. Djirackor, Amanuel Abrdo Tereda, Sun Yi, Younho Seong   +4 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Bayesian Prior Choice in IRT estimation using MCMC and Variational Bayes

Frontiers in Psychology, 2016
This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (
Prathiba Natesan, Ratna Nandakumar, Tom Minka, Jonathan Rubright   +3 more
doaj   +1 more source

Steve: A Hierarchical Bayesian Model for Supernova Cosmology [PDF]

The Astrophysical Journal, 2019
Abstract We present a new Bayesian hierarchical model (BHM) named Steve for performing Type Ia supernova (SN Ia) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency.
S. R. Hinton, T. M. Davis, A. G. Kim, D. Brout, C. B. D’Andrea, R. Kessler, J. Lasker, C. Lidman, E. Macaulay, A. Möller, M. Sako, D. Scolnic, M. Smith, R. C. Wolf, M. Childress, E. Morganson, S. Allam, J. Annis, S. Avila, E. Bertin, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, C. E. Cunha, L. N. da Costa, C. Davis, J. De Vicente, D. L. DePoy, P. Doel, T. F. Eifler, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztanaga, D. W. Gerdes, R. A. Gruendl, J. Gschwend, G. Gutierrez, W. G. Hartley, D. L. Hollowood, K. Honscheid, E. Krause, K. Kuehn, N. Kuropatkin, O. Lahav, M. Lima, M. A. G. Maia, M. March, J. L. Marshall, F. Menanteau, R. Miquel, R. L. C. Ogando, A. A. Plazas, E. Sanchez, V. Scarpine, R. Schindler, M. Schubnell, S. Serrano, I. Sevilla-Noarbe, M. Soares-Santos, F. Sobreira, E. Suchyta, G. Tarle, D. Thomas, V. Vikram, Y. Zhang   +68 more
openaire   +10 more sources

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Bayesian hierarchical vector autoregressive models for patient-level predictive modeling.

PLoS ONE, 2018
Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling.
Feihan Lu, Yao Zheng, Harrington Cleveland, Chris Burton, David Madigan   +4 more
doaj   +1 more source

Bayesian hierarchical statistical SIRS models

Statistical Methods & Applications, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lili Zhuang, Noel Cressie
openaire   +3 more sources

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source