These open-source MMM tools solve different measurement problems, from budget optimization to forecasting and preprocessing.

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 ...

Betting sites will begin posting their initial point spreads for the November 22 Cal-Stanford game late Saturday evening or Sunday, but while we wait for those betting lines with bated breath, we will ...

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 ...

Objective: This study aims to investigate the association between skeletal muscle mass (SMM) and left ventricular mass (LVM), providing a basis for health management and cardiac health interventions ...

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...

In Table 3, the VIF values for each variable are < 5, which has been reduced as multicollinearity between variables. 3.3. Use the Entropy Weight Method to Weight the Data When exploring the factors ...

Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and ...

1 PSE-SANTE/SESANE/LEPID, Institut de Radioprotection et de Sûreté Nucléaire, Paris, France 2 Université de Paris, Unité de Recherche “Biostatistique, Traitement et Modélisation des données ...

I am trying to develop a Bayesian logistic regression model with pystan and get the posterior probabilities for each observation.. but how can I do? I ran a stan model with the following code: code = ...