Results 111 to 120 of about 758,750 (320)

Topological Properties of International Commodity Market: How Uncertainty Affects the Linkages?

Agribusiness, EarlyView.
ABSTRACT The study aims to explore the network topology of the international commodity market by examining the interconnections among 21 commodity futures across various categories, including energy, precious and industrial metals, and agriculture. We analyze the market structure of these commodity futures under both low and high uncertainty conditions
Ibrahim Yagli, Bayram Deviren
wiley   +1 more source

Applying SEM, Exploratory SEM, and Bayesian SEM to Personality Assessments

Psych
Despite the importance of demonstrating and evaluating how structural equation modeling (SEM), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM) work simultaneously, research comparing these analytic ...
Hyeri Hong, Walter P. Vispoel, Alfonso J. Martinez   +2 more
doaj   +1 more source

Bayesian hierarchical modelling of bacteria growth [PDF]

Bacterial growth models are commonly used in food safety. Such models permit the prediction of microbial safety and the shelf life of perishable foods.
Ana P. Palacios, Juan Miguel Marín, Michael P. Wiper   +2 more
core  

Association Between Liver Function Grade and Post‐Hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma: A Latent Class Analysis

Annals of Gastroenterological Surgery, EarlyView.
We retrospectively analyzed clinical data from patients who underwent hepatectomy for hepatocellular carcinoma (HCC) using LCA‐based grading system. These findings provide a new risk stratification framework for the design of precision surgery to treat patients with HCC.
Ling Liu, Jintao Zheng, Ye Wang, Chenao Yang, Jiachen Zhang, Changku Jia   +5 more
wiley   +1 more source

Longitudinal hierarchical Bayesian models of covariate effects on airway and alveolar nitric oxide. [PDF]

Sci Rep, 2023
Weng J, Molshatzki N, Marjoram P, Gauderman WJ, Gilliland FD, Eckel SP.   +5 more
europepmc   +1 more source

Bayesian hierarchical statistical SIRS models

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

A multiscale Bayesian optimization framework for process and material codesign

AIChE Journal, EarlyView.
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley   +1 more source

A multivariate hierarchical Bayesian approach to measuring agreement in repeated measurement method comparison studies

BMC Medical Research Methodology, 2009
Background Assessing agreement in method comparison studies depends on two fundamentally important components; validity (the between method agreement) and reproducibility (the within method agreement).
Schluter Philip J
doaj   +1 more source

Variational Inference in Nonconjugate Models

, 2013
Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm.
Blei, David M., Wang, Chong
core  

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