Objective To analyse temporal trends of respiratory infectious diseases (RIDs) in Baiyin City from 2014 to 2023, aiming to explore the epidemiological patterns of these diseases (tuberculosis, scarlet ...

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 ...

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

This study proposes an important new approach to analyzing cell-count data that are often undersampled and cannot be correctly assessed with traditional statistical analyses. The presented case ...

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 ...

Bayesian Optimization, widely used in experimental design and black-box optimization, traditionally relies on regression models for predicting the performance of solutions within fixed search spaces.

Machine learning has seen significant advancements in integrating Bayesian approaches and active learning methods. Two notable research papers contribute to this development: “Bayesian vs.

At 80, the model and actress waves the flag for “full-grown women.” Interview by Guy Trebay The Unstoppables is a series about people whose ambition is undimmed by time. Below, Lauren Hutton explains, ...

ABSTRACT: Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric ...