Results 71 to 80 of about 758,750 (320)

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

Bayesian hierarchical models for disease mapping applied to contagious pathologies.

PLoS ONE, 2021
Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose
Sylvain Coly, Myriam Garrido, David Abrial, Anne-Françoise Yao   +3 more
doaj   +1 more source

Dynamically rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models

, 2018
Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models.
Kleppe, Tore Selland
core   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

The Best Fit Bayesian Hierarchical Generalized Linear Model Selection Using Information Complexity Criteria in the MCMC Approach

Journal of Mathematics
Both frequentist and Bayesian statistics schools have improved statistical tools and model choices for the collected data or measurements. Model selection approaches have advanced due to the difficulty of comparing complicated hierarchical models in ...
Endris Assen Ebrahim, Mehmet Ali Cengiz, Erol Terzi   +2 more
doaj   +1 more source

Bayesian hierarchical models combining different study types and adjusting for covariate imbalances: a simulation study to assess model performance.

PLoS ONE, 2011
BackgroundBayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between ...
C Elizabeth McCarron, Eleanor M Pullenayegum, Lehana Thabane, Ron Goeree, Jean-Eric Tarride   +4 more
doaj   +1 more source

Modeling the probability of a batter/pitcher matchup event: A Bayesian approach. [PDF]

PLoS ONE, 2018
We develop a Bayesian hierarchical log5 model to predict the probability of a particular batter/pitcher matchup event in baseball by extending the log5 model which is widely used for describing matchup events.
Woojin Doo, Heeyoung Kim
doaj   +1 more source

Comment: Bayesian Checking of the Second Level of Hierarchical Models: Cross-Validated Posterior Predictive Checks Using Discrepancy Measures

, 2008
Comment: Bayesian Checking of the Second Level of Hierarchical Models [arXiv:0802.0743]Comment: Published in at http://dx.doi.org/10.1214/07-STS235B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics ...
Larsen, Michael D., Lu, Lu
core   +1 more source

On the half-Cauchy prior for a global scale parameter [PDF]

, 2010
This paper argues that the half-Cauchy distribution should replace the inverse-Gamma distribution as a default prior for a top-level scale parameter in Bayesian hierarchical models, at least for cases where a proper prior is necessary.
Polson, Nicholas G., Scott, James G.
core   +3 more sources

Bayesian hierarchical modeling on covariance valued data

Stat, 2023
Analysis of structural and functional connectivity (FC) of human brains is of pivotal importance for diagnosis of cognitive ability. The Human Connectome Project (HCP) provides an excellent source of neural data across different regions of interest (ROIs) of the living human brain.
Satwik Acharyya, Zhengwu Zhang, Anirban Bhattacharya, Debdeep Pati   +3 more
openaire   +4 more sources