Results 1 to 10 of about 157,491 (182)

Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Sensors, 2020
Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data.
Mingming Song, Iman Behmanesh, Babak Moaveni, Costas Papadimitriou   +3 more
doaj   +3 more sources

Bayesian Hierarchical Modeling for Variance Estimation in Biopharmaceutical Processes [PDF]

Bioengineering
Determining process variances in biopharmaceutical manufacturing is challenging due to limited data availability. To address this, we introduce a Bayesian hierarchical model designed for meta-analysis of process variance.
Sonja Schach, Tobias Eilert, Beate Presser, Marco Kunzelmann   +3 more
doaj   +2 more sources

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

NeuroImage, 2022
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach.
Johanna M.M. Bayer, Richard Dinga, Seyed Mostafa Kia, Akhil R. Kottaram, Thomas Wolfers, Jinglei Lv, Andrew Zalesky, Lianne Schmaal, Andre Marquand   +8 more
doaj   +3 more sources

Bayesian hierarchical models for multivariate mixed responses with repeated measures: A case study in arterial occlusive disease. [PDF]

PLoS ONE
Modeling repeated measures of arterial occlusive diseases, such as peripheral artery disease (PAD), using data with mixed-type outcomes poses unique challenges due to complex dependency structures and diverse distributional assumptions.
Endris Assen Ebrahim, Mehmet Ali Cengiz
doaj   +2 more sources

Modeling Error Estimation and Response Prediction of a 10-Story Building Model Through a Hierarchical Bayesian Model Updating Framework

Frontiers in Built Environment, 2019
In this paper a hierarchical Bayesian model updating approach is proposed for calibration of model parameters, estimation of modeling error, and response prediction of dynamic structural systems.
Mingming Song, Iman Behmanesh, Babak Moaveni, Costas Papadimitriou   +3 more
doaj   +3 more sources

dockerHDDM: A User-Friendly Environment for Bayesian Hierarchical Drift-Diffusion Modeling

Advances in Methods and Practices in Psychological Science
Drift-diffusion models (DDMs) are pivotal in understanding evidence-accumulation processes during decision-making across psychology, behavioral economics, neuroscience, and psychiatry. Hierarchical DDMs (HDDMs), a Python library for hierarchical Bayesian
Wanke Pan, Haiyang Geng, Lei Zhang, Alexander Fengler, Michael J. Frank, Ru-Yuan Zhang, Hu Chuan-Peng   +6 more
doaj   +2 more sources

Estimation of Parental Abundance Using Hierarchical Bayesian Modeling With Data Augmentation [PDF]

Ecology and Evolution
Pedigree‐based estimation methods leverage the fact that each offspring in a cohort is genotypically “marked” by its parents and represent a recent and promising toolset for estimating population dynamics.
Benjamin Marcy‐Quay, Nicholas M. Sard
doaj   +2 more sources

Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie

Ecological Indicators, 2021
We developed a Bayesian hierarchical modeling framework to establish a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using chlorophyll a concentration as the predictor.
Song S. Qian, Craig A. Stow, Freya E. Rowland, Qianqian Liu, Mark D. Rowe, Eric J. Anderson, Richard P. Stumpf, Thomas H. Johengen   +7 more
doaj   +1 more source

Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

Future Internet, 2021
Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions.
Taghreed Alghamdi, Khalid Elgazzar, Taysseer Sharaf   +2 more
doaj   +1 more source

Using hierarchical Bayesian methods to examine the tools of decision-making [PDF]

Judgment and Decision Making, 2011
Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task.
Michael D. Lee, Benjamin R. Newell, Andreas Glöckner, Benjamin E. Hilbig   +3 more
doaj   +2 more sources