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 ...

Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.

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 ...

The Bayesian Learning Consortium is an industry-sponsored research consortium focusing on advanced quantitative methods for subsurface characterization. The goal of the consortium is to develop ...

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 ...