Results 51 to 60 of about 2,165 (200)

A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction

G3: Genes, Genomes, Genetics, 2017
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account ...
Osval A. Montesinos-López, Abelardo Montesinos-López, José Crossa, Fernando H. Toledo, José C. Montesinos-López, Pawan Singh, Philomin Juliana, Josafhat Salinas-Ruiz   +7 more
doaj   +1 more source

Which severe COVID-19 patients could benefit from high dose dexamethasone? A Bayesian post-hoc reanalysis of the COVIDICUS randomized clinical trial

Annals of Intensive Care, 2023
Background The respective benefits of high and low doses of dexamethasone (DXM) in patients with severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) and acute respiratory failure (ARF) are controversial, with two large triple-blind RCTs reaching ...
Sylvie Chevret, Lila Bouadma, Claire Dupuis, Charles Burdet, Jean-François Timsit, the COVIDICUS RCT group   +5 more
doaj   +1 more source

Graph‐Laplacian modeling of spatiotemporal effects for house price estimation

Real Estate Economics, EarlyView.
Abstract Many variables involve the modeling of spatial effects, and their dynamics over time. This article presents a linear model in which spatiotemporal random effects are modeled by graph‐Laplacians. A graph‐Laplacian flexibly encodes adjacency in both space and time, in our case not depending on unknown parameters. The graph‐Laplacian can be input
Willem P Sijp, Marc K. Francke
wiley   +1 more source

To vary or not to vary: A flexible empirical Bayes factor for testing variance components

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Random effects are the gold standard for capturing structural heterogeneity, such as individual differences or temporal dependence. Yet testing their presence is difficult because variance components are constrained to be non‐negative, creating a boundary problem. This paper introduces a flexible empirical Bayes factor (EBF) for testing random
Fabio Vieira, Hongwei Zhao, Joris Mulder
wiley   +1 more source

Comparative noninformativities of quantum priors based on monotone metrics [PDF]

Physics Letters A, 1998
7 pages, LaTeX, minor changes, to appear in Physics Letters ...
openaire   +3 more sources

On Integral Priors for Multiple Comparison in Bayesian Model Selection

International Statistical Review, EarlyView.
Summary Noninformative priors constructed for estimation purposes are usually not appropriate for model selection and testing. The methodology of integral priors was developed to get prior distributions for Bayesian model selection when comparing two models, modifying initial improper reference priors. We propose a generalisation of this methodology to
Diego Salmerón, Juan Antonio Cano, Christian P. Robert   +2 more
wiley   +1 more source

Early Discrimination of Maternal Haploid and Diploid Maize (Zea mays L.) Seedlings Using Morphological Traits and Random Forest Classifier

Plant Breeding, EarlyView.
ABSTRACT Doubled haploid (DH) technology has been widely adopted in maize (Zea mays L.) breeding programs due to its ability to reduce breeding cycle time and optimize costs. Early ploidy identification is essential for maximizing the efficiency of DH production, particularly prior to chromosome doubling.
Mariana Martins Marcondes, Matheus Lucas Schuck, Victor Hugo Garcia, Robson Marcelo Rossi, Vanderlei Aparecido de Lima, Rodrigo Gomes, Carlos Alberto Scapim, Marcos Ventura Faria   +7 more
wiley   +1 more source

Simple Marginally Noninformative Prior Distributions for Covariance Matrices

Bayesian Analysis, 2013
A family of prior distributions for covariance matrices is studied. Members of the family possess the attractive property of all standard deviation and correlation parameters being marginally noninformative for particular hyper-parameter choices. Moreover, the family is quite simple and, for approximate Bayesian inference techniques such as Markov ...
Huang, Alan, Wand, M. P.
openaire   +5 more sources

VQ‐Wave: A Physics‐Driven Spatiotemporal Deep Learning Approach for Noncontrast‐Enhanced Lung Ventilation and Perfusion MRI

Magnetic Resonance in Medicine, Volume 96, Issue 3, Page 1427-1442, September 2026.
ABSTRACT Purpose To develop a robust deep learning framework for noncontrast‐enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiological nonstationarity. Methods We introduce VQ‐Wave (Ventilation/Q‐perfusion Waveform‐based Assessment of Variable Evolutions), a physics‐driven spatiotemporal ...
Grzegorz Bauman, Pavlos Panos, Philipp Latzin, Oliver Bieri   +3 more
wiley   +1 more source

Informative priors or noninformative priors? A Bayesian re-analysis of binary data from Macugen phase III clinical trials

, 2017
It has became more popular in the recent statistical literature to see Bayesian approaches for clinical trials, such as assurance calculations for study designs and the use of posterior probability for data analyses.
Ting, N., Chen, D.-G., Ho, S.
core   +1 more source