The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...
EurekAlert!

Journal of Environmental Sciences study reveals how artificial intelligence can transform PM2.5 monitoring

Finely dispersed particulate matter with a diameter of ≤2.5 μm (PM2.5) poses a significant health- and climate-risk, yet ...
GitHub

Spatiotemporal heterogeneous perspective for analyzing influencing factors, identifying key drivers, and making dynamic predictions, all within a unified ‘full-map’ framework.

The package is currently in the internal testing phase. At present, it only supports local installation from GitHub. The BSTVC R package is designed to provide a comprehensive suite of functionalities ...

BioStem Technologies, Inc.: BioStem Technologies Highlights Alignment with FDA's Openness to Bayesian Statistical Approaches in Clinical Research

BioStem Technologies, Inc. (OTC: BSEM), a leading MedTech company focused on the development, manufacturing, and commercialization of perinatal ...

BioStem Technologies Highlights Alignment with FDA’s Openness to Bayesian Statistical Approaches in Clinical Research

BioStem remains committed to advancing evidence-based innovation in wound care through rigorous clinical research and real-world data analysis, supported by its proprietary BioRetain ® process and ...
IEEE

Using Bayesian Regression for Stacking Time Series Predictive Models

Abstract: The paper describes the use of Bayesian inference for stacking regression of different predictive models for time series. The models ARIMA, Neural Network, Random Forest, Extra Tree were ...
Scientific Research Publishing

Hahn, P.R., Murray, J.S. and Carvalho, C.M. (2017) Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects.

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...
GitHub

psoerensen/bayesian-linear-regression

This repository includes theoretical notes, slides, and hands-on R examples for exploring Bayesian Linear Regression. It introduces both classical and Bayesian regression methods, showing how to ...
Scientific Research Publishing

Hahn, P.R., Murray, J.S. and Carvalho, C.M. (2020) Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion ...

ABSTRACT: This study investigates the persistent academic impacts of the Head Start program, a federal government-funded early childhood intervention, using data from the Early Childhood Longitudinal ...
marktechpost

Google AI’s New Regression Language Model (RLM) Framework Enables LLMs to Predict Industrial System Performance Directly from Raw Text Data

Predicting performance for large-scale industrial systems—like Google’s Borg compute clusters—has traditionally required extensive domain-specific feature engineering and tabular data representations, ...