A new study combines drone data, satellite observations, and ground-based flux measurements to examine methane emissions from ...

US FDA issues guidance on modernizing statistical methods for clinical trials: Maryland Wednesday, January 14, 2026, 09:00 Hrs [IST] The US Food and Drug Administration today publ ...

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 ...

In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a ...

Receive the the latest news, research, and presentations from major meetings right to your inbox. TCTMD ® is produced by the Cardiovascular Research Foundation ® (CRF). CRF ® is committed to igniting ...

I did not find an example using DoWhy to do inference and variable manipulation on a hybrid network, which has both categorical and continuous variables. I tried the ...

Department of Physics, Arizona State University, Tempe, Arizona 85281, United States Center for Biological Physics, Arizona State University, Tempe, Arizona 85281, United States College of Medical and ...

Objectives: We aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD). Methods: We included 18 people with PD and 18 controls.

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

The mathematics that enable sensor fusion include probabilistic modeling and statistical estimation using Bayesian inference and techniques like particle filters, Kalman filters, and α-β-γ filters, ...