Abstract: In this paper, a new extension of the Rayleigh-Weibull (RW) model, called the sine Rayleigh-Weibull (SRW) model, is proposed. This model is constructed using the trigonometrically generated ...

Abstract: A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including ...

Bayesian methods for inference and prediction have become widespread in the social sciences (and beyond). Over the last decades, applied Bayesian modeling has evolved from a niche methodology with ...

For whom? The events are open to all interested, within or outside of KI. The events are free of charge. The program is tailored towards users of statistics (but you don’t need to be a statistician), ...

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 ...

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 ...

In the lead up to this year’s presidential election, Andrew Gelman, a professor of political science and statistics, collaborated with Ben Goodrich, an instructor in the political science department, ...