Results 101 to 110 of about 157,640 (328)

spTimer: Spatio-Temporal Bayesian Modeling Using R

Journal of Statistical Software, 2015
Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power.
Khandoker Shuvo Bakar, Sujit K. Sahu
doaj   +1 more source

Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks

, 2019
Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks.
Amini, Arash A., Lin, Lizhen, Paez, Marina S.   +2 more
core  

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Bayesian nested frailty model for evaluating surgical management of patulous Eustachian tube dysfunction

BMC Medical Research Methodology
Background The nested frailty model, a random effects survival model that can accommodate data clustered at two hierarchical levels, has been rarely used in practice.
Kosuke Kawai, Bryan K. Ward, Joonas Toivonen, Dennis S. Poe   +3 more
doaj   +1 more source

Hierarchical Implicit Models and Likelihood-Free Variational Inference

, 2017
Implicit probabilistic models are a flexible class of models defined by a simulation process for data. They form the basis for theories which encompass our understanding of the physical world.
Blei, David M., Ranganath, Rajesh, Tran, Dustin   +2 more
core  

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

Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling

Clinical and Translational Science
Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy.
Ahmed Elmokadem, Matthew Wiens, Timothy Knab, Kiersten Utsey, Samuel P. Callisto, Daniel Kirouac   +5 more
doaj   +1 more source

Considering groups in the statistical modeling of spatio-temporal data

Statistica, 2013
Spatio-temporal statistical methods are developing into an important research topic that goes beyond the study of processes that generate independent, identically distributed observations.
Daniela Cocchi, Francesca Bruno
doaj   +1 more source

Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada [PDF]

The Truckee / Carson / Walker River Watershed in Northern Nevada is under an imminent threat of infestation by the New Zealand Mud Snail, an aquatic nuisance species with the potential to harm recreational fisheries. We combine a utility-theoretic system-
Allison Davis, Klaus Moeltner
core  

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