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 ...

IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...

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 ...

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 ...

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 ...

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 ...