Abstract: Machine learning is widely used to predict software defect-prone components, facilitating testing and improving application quality. In a recent meta-analysis on binary classification for ...

Discover how Fourier Analysis breaks down complex time series data into simpler components to identify trends and patterns, despite its limitations in stock forecasting.

Background The relationship of social determinants of health (SDOH), environmental exposures and medical history to lung function trajectories is underexplored. A better understanding of these ...

Purpose: Bayesian approaches may improve the efficiency of trials and accelerate decision-making, but reluctance to depart from traditional frequentist statistics may limit their use. Because oncology ...

Abstract: Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties. Most previous studies on gas ...

This webinar introduces healthcare researchers to Bayesian meta-analysis methods, challenging the perception that these methods are inaccessible to non-statistical researchers. The session ...

ABSTRACT: This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, ...

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including ...

Kevin Dayaratna was Director of the Center for Data Analysis and Chief Statistician for Heritage. A variety of statistical tools can detect potential breaches in the integrity of elections. These ...

While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to ...