Results 171 to 180 of about 548,736 (273)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Navigating the Bayes maze: The psychologist's guide to Bayesian statistics, a hands-on tutorial with R code. [PDF]

Int J Psychol
Alter U, Too MA, Cribbie RA.
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Fair Data, Bayesian Statistics and Human Cohort Studies: Current Trends in Metabolomic Research. [PDF]

Metabolites
Fiehn O.
europepmc   +1 more source

The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers

Advanced Intelligent Discovery, EarlyView.
We assess the maturity and integration readiness of key methodologies for Materials Acceleration Platforms, highlighting the need for dynamic workflow managers. Demonstrating this, we integrate PerQueue into a color‐mixing robot, showing how flexible orchestration improves coordination and optimization.
Simon K. Steensen, Patrick W. V. Butler, Jesper R. Pedersen, Nis Fisker‐Bødker, Tejs Vegge, Jin Hyun Chang, Ivano E. Castelli   +6 more
wiley   +1 more source

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review. [PDF]

BMC Med Res Methodol
Lee SY.
europepmc   +1 more source

CALX-CBD1 Ca²⁺-Binding Cooperativity Studied by NMR Spectroscopy and ITC with Bayesian Statistics. [PDF]

Biophys J, 2020
Cardoso MVC, Rivera JD, Vitale PAM, Degenhardt MFS, Abiko LA, Oliveira CLP, Salinas RK.   +6 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Applying causal inference and Bayesian statistics to understanding vaccine safety signals using a simulation study. [PDF]

NPJ Vaccines
Tay E, Dymock M, Lopez L, Glover C, Huang YA, Bakar KS, Snelling T, Marsh JA, Wu Y.   +8 more
europepmc   +1 more source

All tests are imperfect: Accounting for false positives and false negatives using Bayesian statistics. [PDF]

Heliyon, 2020
Qian SS, Refsnider JM, Moore JA, Kramer GR, Streby HM.   +4 more
europepmc   +1 more source