In the evolving landscape of artificial intelligence (AI), the assumption that more data lead to better models has driven unchecked reliance on synthetic data to augment training datasets. Although ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...

Abstract: Bayesian Federated Learning (FL) policies enable multiple nodes to collaboratively train a shared Machine Learning (ML) model while accounting for the uncertainty of its predictions. This is ...

ABSTRACT: This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian Generalized Additive Mixed Models (GAMMs).

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors. This work proposes a ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Synthetic dataset outputs for public analysis without privacy risk. Part of my current workflow as survey leader of the Data Engineering Pilipinas group. Comparable distributions per column: based on ...

Keizo Asami Institute, iLIKA, Federal University of Pernambuco, Recife, Pernambuco 50670-901, Brazil Graduate Program in Biology Applied to Health, PPGBAS, Federal University of Pernambuco, Recife, ...

Google has updated its AI-powered database fleet management offering — Database Center — with the capability to monitor self-managed databases running on its own compute virtual machines (VMs).