Results 181 to 190 of about 442,883 (292)

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

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

A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models. [PDF]

Bull Math Biol
Cassidy T, Johnston ST, Plank M, Botha I, Flegg JA, Murphy RJ, Hamis S.   +6 more
europepmc   +1 more source

Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel-contaminated soil. [PDF]

Ecol Evol, 2015
Arbel J, King CK, Raymond B, Winsley T, Mengersen KL.   +4 more
europepmc   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +1 more source

Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models. [PDF]

Stat Med
Orihara S, Sugasawa S, Ohigashi T, Hirano K, Nakagawa T, Taguri M.   +5 more
europepmc   +1 more source

Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck, Samuel Yanes, Manuel Perales, Daniel Gutiérrez, Sergio Toral   +4 more
wiley   +1 more source

Wasserstein GAN-based estimation for conditional distribution function with current status data. [PDF]

Lifetime Data Anal
Su W, Liu C, Yin G, Huang J.
europepmc   +1 more source

Phylogenomics, ecomorphological evolution, and historical biogeography in Deuterocohnia (Bromeliaceae: Pitcairnioideae)

American Journal of Botany, EarlyView.
Abstract Premise Species of Deuterocohnia (17 spp.) show extraordinary variation in elevation (0–3900 m a.s.l.) and growth forms, and many have narrow geographic distributions in the west‐central Andes and the Peru‐Chile coast. Previous research using few plastid and nuclear loci failed to produce well‐resolved or supported phylogenies.
Bing Li, Nicole Schütz, Kurt Weising, Georg Zizka, Jacob B. Landis, Thomas J. Givnish   +5 more
wiley   +1 more source

Extensions of multinomial processing tree models for continuous variables: A simulation study comparing parametric and non-parametric approaches. [PDF]

Behav Res Methods
Gutkin A, Heck DW.
europepmc   +1 more source