Results 91 to 100 of about 539,449 (277)

Bayesian model selection for group studies — Revisited

NeuroImage, 2014
In this paper, we revisit the problem of Bayesian model selection (BMS) at the group level. We originally addressed this issue in Stephan et al. (2009), where models are treated as random effects that could differ between subjects, with an unknown population distribution.
Rigoux, Lionel, Stephan, Klaas E, Friston, Karl J, Daunizeau, Jean   +3 more
openaire   +3 more sources

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

Bayesian Robust Inference of Sample Selection Using Selection-t Models

, 2014
Heckman selection model is the most popular econometric model in analysis of data with sample selection. However, selection models with Normal errors cannot accommodate heavy tails in the error distribution.
Ding, Peng
core   +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

Selection of tuning parameters in bridge regression models via Bayesian information criterion

, 2012
We consider the bridge linear regression modeling, which can produce a sparse or non-sparse model. A crucial point in the model building process is the selection of adjusted parameters including a regularization parameter and a tuning parameter in bridge
A Antoniadis, AE Hoerl, C Park, C-H Zhang, CM Hurvich, CM Hurvich, G McDonald, G Schwarz, H Zou, H Zou, J Fan, J Fan, J Huang, J Huang, J Lv, JE Frank, K Knight, L Tierney, M Ishiguro, M Yuan, N Sugiura, P Bühlmann, P Craven, PHC Eilers, R Tibshirani, S Konishi, Shuichi Kawano, T Shimamura, WJ Fu, Y Liu   +29 more
core   +1 more source

Model Selection for Bayesian Autoencoders

, 2021
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance ...
Tran, Ba-Hien, Rossi, Simone, Milios, Dimitrios, Michiardi, Pietro, Bonilla, Edwin V., Filippone, Maurizio   +5 more
openaire   +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

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Bayesian Model Selection Maps for Group Studies

NeuroImage, 2009
This technical note describes the construction of posterior probability maps (PPMs) for Bayesian model selection (BMS) at the group level. This technique allows neuroimagers to make inferences about regionally specific effects using imaging data from a group of subjects.
Rosa, M J, Bestmann, S, Harrison, L, Penny, W   +3 more
openaire   +3 more sources

Precise Control of Drug Release in Machine Learning‐Designed Antibody‐Eluting Implants for Postoperative Scarring Inhibition in Glaucoma

Advanced Healthcare Materials, EarlyView.
We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin, Wenbing Jiang, Kai Xin Thong, Zeynep Ulker, Brihitejas Patel, Cynthia Yu‐Wai‐Man   +5 more
wiley   +1 more source