The US Food and Drug Administration (FDA) is now “open to bayesian statistics,” contrasting this with the frequentist approach that the agency and the drug industry have historically relied on for ...

Abstract: Sparse Bayesian learning (SBL) is an advanced statistical framework that dominantly enhances the sparse features of targets of interest in radar imagery. A widely adopted strategy for ...

Incrementality testing in Google Ads is suddenly within reach for far more advertisers than before. Google has lowered the barriers to running these tests, making lift measurement possible even ...

In recent years, something unexpected has been happening in artificial intelligence. Modern AI appears to be breaking a rule that statisticians have preached for nearly a century: Keep models in a ...

Project Overview This repository demonstrates statistical computing concepts across three programming languages (Python, Julia, and R). The project contains 57 total files (19 per language) covering ...

Objectives: We aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD). Methods: We included 18 people with PD and 18 controls.

Bayesian methods for inference and prediction have become widespread in the social sciences (and beyond). Over the last decades, applied Bayesian modeling has evolved from a niche methodology with ...

Abstract: Localized statistical channel modeling (LSCM) is an efficient channel modeling framework recently proposed for wireless network optimization which learns the angular power spectrum (APS) of ...

The mathematics that enable sensor fusion include probabilistic modeling and statistical estimation using Bayesian inference and techniques like particle filters, Kalman filters, and α-β-γ filters, ...

In the wake of the replication crisis, statistical power has become one of the central issues in debates about the quality of research. The widespread use of tests with low power is seen as a key ...