Bayesian Analysis Science
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis
Science fundamentally depends on the generation and testing of hypotheses, many of them controversial. An explosion in scientific literature has made evaluating hypotheses even within a domain a problem of scale, and risks slowing an already extensive consensus-building process. While this challenge has prompted interest in automated hypothesis evaluation tools, existing methods have not yet proven effective for comparing hypotheses.
Systematic estimates of global causes of neonatal and under 5 mortality in 2000-24: secondary data analysis using bayesian multinomial logistic regression
AbstractObjectiveTo estimate cause specific mortality among neonates and children under 5 for 195 countries from 2000 to 2024.DesignSecondary data analysis using a Bayesian multinomial logistic regression model to estimate cause specific mortality fractions. Data sourcesPubMed, Embase, Web of Science, SCOPUS, Cochrane, Global Health Index Medicus, PAHO, Global Health OVID, Africa-Wide Information, IndMed, WHO Mortality Database, Demographic and Health Surveys (DHS), Multiple Indicator...
Estimates of global causes of death for children and adolescents aged 5-19 in 2000-24: secondary data analysis using bayesian multinomial logistic regression
AbstractObjectiveTo estimate cause specific mortality among children and adolescents aged 5-19 years for 195 countries from 2000 to 2024.DesignSecondary data analysis using a bayesian multinomial logistic regression model to estimate cause specific mortality fractions. Data sourcesPubMed, Embase, Web of Science, Scopus, Cochrane Library, Global Index Medicus, Pan American Health Organization, Global Health Ovid, Africa-Wide Information, IndMed, WHO Mortality Database, Demographic and Health...
Ontologizer 3: a cross-platform desktop application for frequentist and Bayesian GO enrichment analysis
We present Ontologizer 3, an easy-to-use cross-platform desktop application for Gene Ontology (GO) overrepresentation analysis. Ontologizer 3 offers two complementary methods. The first is a frequentist approach that evaluates GO terms individually using a one-sided Fisher's exact test, yielding term-level significance values.
From data to decisions: Bayesian modelling and global sensitivity analysis for flotation control
arXiv:2606.06173v1 Announce Type: new Abstract: This work presents a data-driven framework for interpretable modelling and decision support in flotation systems, integrating Gaussian Process (GP) regression with Global Sensitivity Analysis (GSA) via Sobol indices and local interpretability using SHapley Additive exPlanations (SHAP). Based on laboratory-scale experimental data, a static GP surrogate model is developed to capture how superficial air velocity, overflowing froth velocity, froth...
Confidence, Statistical Evidence and Relative Belief with Applications to a Problem in Particle Physics
Announce Type: new Abstract: Probability theory provides a clear definition of what is meant by evidence in favor, against or none either way, of an event occurring for an unobserved response, via the principle of evidence. This is immediately applicable when carrying out a proper Bayesian analysis.
Scale-Free Priors and Survival Dynamics: A Bayesian Framework for Conflict Duration
arXiv:2606.01328v1 Announce Type: cross Abstract: We have developed a fully Bayesian survival-analysis framework that reformulates inference about system lifetimes in terms of hazard and survival functions, and extends this representation to interacting actors. Starting from J.~Richard Gott's Copernican principle, we express the scale-free prior as a baseline hazard $\lambda(t)=1/t$, thereby linking a static prior over lifetimes to the dynamic language of survival analysis. In this...
PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
arXiv:2606.06293v1 Announce Type: new Abstract: Whilst the vulnerability of graph neural networks (GNNs) to adversarial attacks poses a critical threat to graph representation learning, the understanding of the robust generalization behavior remains a fundamental challenge in the adversarial setting. Recently, PAC-Bayesian margin-based generalization analysis substantially advances this line of research by providing a flexible and data-dependent analytical framework. However, existing robust...
Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors
Announce Type: replace Abstract: Derivative-free Bayesian inversion arises in science and engineering applications, particularly when forward model is costly or infeasible to differentiate through. Existing derivative-free methods collapse the posterior to a point estimate or return severely over-confident uncertainty on high-dimensional, nonlinear problems. We introduce Blade, which produces accurate and well-calibrated posteriors using an ensemble of interacting particles.
Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
arXiv:2606.00783v1 Announce Type: cross Abstract: Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Ghana, health-facility data from 2014 to 2023 reveal non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture stochastic variability or provide credible uncertainty bounds. This study develops a Bayesian nonlinear inference framework that...