Concr CEO Irina Babina and CTO Matthew Griffiths unpack how Bayesian foundation models can excel at uncertainty management to ...

As rare disease trials face persistent feasibility challenges, Bayesian designs are gaining momentum by enabling more flexible, data-driven approaches that integrate prior knowledge, reduce sample ...

In this video interview, David Morton, PhD, director of biostatistics at Certara, explains how regulatory momentum is ...

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 ...

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, ...

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, ...