Bayesian prediction and modeling have emerged as transformative tools in the design and management of clinical trials. By integrating prior knowledge with accumulating trial data, Bayesian methods ...

In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term ...

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, ...

A peer-reviewed article in Neurobiology of Learning and Memory is challenging a foundational assumption about how animals and ...

Google Research has proposed a training method that teaches large language models to approximate Bayesian reasoning by learning from the predictions of an optimal Bayesian system. The approach focuses ...

Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks.

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, ...

Extended educational sessions that offer attendees the opportunity to learn research methods and techniques from prominent psychological scientists.