Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

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 ...

In the Big Data era, many scientific and engineering domains are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, ...

a.s.ist today announced the launch of a 7-day free trial for AutoStatSpectra, its Bayesian-statistics-based spectral analysis ...

To address the limitations of binary pCR classification, Pusztai and colleagues developed the residual cancer burden (RCB) ...

The US FDA has issued draft guidance supporting the use of Bayesian statistical models in drug and biologic trials, enabling sponsors to integrate prior and real-world data into study designs. This ...

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 ...

A new Bayesian statistical framework uses the residual cancer burden score to better predict long-term survival in breast ...

Combining dogs' powerful sense of smell with advanced analytics, researchers are testing a non-invasive method to detect ...