Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

Artificial intelligence can solve problems at remarkable speed, but it's the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists ...

Learn to apply Bayes' theorem in financial forecasting for insightful, updated predictions. Enhance decision-making with effectively modeled probabilities.

Median regression models become an attractive alternative to mean regression models when employing flexible families of distributions for the errors. Classical ...

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about ...

In this video from PyCon Australia, Rhydwyn McGuire from the The New South Wales Department of Health presents: Video: Fast, Beautiful and Easy Bayesian Modeling – Can You have it all? Bayesian models ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

Risk perceptions in localized breast cancer (BC). This is an ASCO Meeting Abstract from the 2013 ASCO Annual Meeting I. This abstract does not include a full text component.