Results 171 to 180 of about 184,299 (283)

Empirical Bayes method using surrounding pixel information for number and brightness analysis. [PDF]

Biophys J, 2021
Fukushima R, Yamamoto J, Kinjo M.
europepmc   +1 more source

Comparison of Crash Modification Factors for Engineering Treatments Estimated by Before–After Empirical Bayes and Propensity Score Matching Methods

, 2022
Bo Lan, Raghavan Srinivasan
openalex   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

A Parametric Empirical Bayes Approach to Personalized Reference Intervals and Reference Change Values. [PDF]

Clin Chem
Røys EÅ, Viste K, Farrell CJ, Kellmann R, Alaour B, Sylte MS, Torsvik J, Strand H, Marber M, Omland T, Theodorsson E, Jones GRD, Aakre KM.   +12 more
europepmc   +1 more source

Using Empirical Bayes Estimation Approach to Quantify the Abnormality of Traffic Conditions During the Covid-19 Pandemic

, 2023
Tumlumbe Juliana Chengula, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi, Judy Perkins   +4 more
openalex   +1 more source

Covariance Structure Modeling of Engineering Demand Parameters in Cloud‐Based Seismic Analysis

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Probabilistic seismic demand modeling aims to estimate structural demand as a function of ground motion intensity—a critical stage in seismic risk assessment. Although many models exist to describe the structural demand, few consider the covariance among engineering demand parameters, potentially overlooking a key factor in improving the ...
Archie Rudman, Enrico Tubaldi, Roberto Gentile, John Douglas   +3 more
wiley   +1 more source

Quantifying Model Selection Uncertainty in Structural Analysis: Methodology and Application

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT With increasing focus on complex engineering systems under rare events, computational models are critical for predictions due to the scarcity or absence of data. However, selecting an appropriate model can be challenging. Using a single model without available test calibration could result in significant bias in performance predictions. A case
Ya‐Heng Yang, Tracy C. Becker
wiley   +1 more source

An empirical Bayes approach for constructing confidence intervals for clonality and entropy. [PDF]

J Appl Stat
Chen Z, Tian L, Olshen RA.
europepmc   +1 more source

A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator. [PDF]

Am J Epidemiol, 2021
Sanyal N, Napolioni V, de Rochemonteix M, Belloy ME, Caporaso NE, Landi MT, Greicius MD, Chatterjee N, Han SS.   +8 more
europepmc   +1 more source

Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions [PDF]

, 2009
Radojka M. Savić, Mats O. Karlsson
openalex   +1 more source