Opinion
The LancetOpinion

Synthetic data, synthetic trust: navigating data challenges in the digital revolution

In the evolving landscape of artificial intelligence (AI), the assumption that more data lead to better models has driven unchecked reliance on synthetic data to augment training datasets. Although ...
The Lancet

Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...
IEEE

Design of Intelligent Analysis Method for E-Commerce Data Based on Improved Bayesian Algorithm

Abstract: It is frequently difficult to handle large-scale, high-dimensional data using traditional e-commerce data analysis methodologies, which makes it challenging to effectively capture user ...
IEEE

Distributed Graph Learning From Smooth Data: A Bayesian Framework

Abstract: The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph Signal Processing (GSP) and finds applications in various data ...
GitHub

Bayesian Statistical Modeling with Stan, R, and Python

Kentaro Matsuura (2023). Bayesian Statistical Modeling with Stan, R, and Python. Singapore: Springer. URL: https://link.springer.com/book/10.1007/978-981-19-4755-1 ...
ahajournals.org

Abstract 4365576: Forecasting Ischemic Heart Disease Burden in Southeast Asia: A Temporal Trend Analysis from 1990 to 2019 and Projections to 2050: A Bayesian Age-Period-Cohort ...

Background: Ischemic heart disease (IHD) remains the leading cause of mortality worldwide, with a rising toll in low- and middle- income countries. Southeast Asia, which is undergoing rapid ...