Fiducial inference is a framework for assigning a probability distribution to an unknown parameter on the basis of observed data and a statistical model alone, without invoking a prior distribution.

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...

Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference ...

Runware, an AI inference and AI-native compute company, today unveiled the Sonic Inference Pod, a modular data center ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...

DTSA 5001 Probability and Foundations for Data Science and AI - Same as APPA 5001 DTSA 5002 Statistical Estimation for Data Science and AI - Same as APPA 5003 DTSA 5003 Statistical Inference and ...

Causal Inference in Oncology Comparative Effectiveness Research Using Observational Data: Are Instrumental Variables Underutilized? The following represents disclosure information provided by authors ...