There was an error while loading. Please reload this page.

Imagine a scenario where a team of doctors faces a perplexing medical puzzle. A patient shows a range of symptoms, each pointing to multiple possible diseases. How can they navigate this diagnostic ...

Bloomberg is pleased to announce the newest cohort of three early-career researchers who have received the Bloomberg Data Science Ph.D. Fellowship for 2024-2025. Now in its seventh cohort, the ...

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...

Some of the material on this web page is based upon work supported by the National Science Foundation under Grants SES-0350686, SES-0719055, and . Any opinions, findings and conclusions or ...

Understanding the interplay between network architecture, dataset statistics, and learning algorithms is a key challenge in deep learning. We overcome this challenge analytically for zero-noise ...

Abstract: In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where ...

Choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process and are required for common statistical tasks such as parameter estimation, ...

The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion ...