Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint proteins known as transcriptional regulators that control how genes turn on ...

Artificial intelligence (AI) increasingly powers safety-critical systems that demand robust, energy-efficient computation, often under conditions of data scarcity and uncertainty. Traditional AI ...

Non-Bayesian learning in social networks refers to a paradigm in which distributed agents iteratively update probabilistic beliefs about an underlying state by combining private observations with the ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

When a computer scientist publishes genetics papers, you might think it would raise colleagues’ eyebrows. But Daphne Koller’s research using a once obscure branch of probability theory called Bayesian ...

New FDA guidance on the use of Bayesian statistics signals a broader shift in accommodating more flexible clinical trial ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...