This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It ...

Abstract: In this paper, Python programming is employed to study the electromagnetic finite element method (FEM) and Bayesian deep learning. Rectangular cavity and folded waveguide (FWG) slow-wave ...

Learn how prior probability informs economic theory and decision-making in Bayesian statistics. Understand its role before collecting new data.

An interdisciplinary team of University of Tennessee, Knoxville researchers recently published in Biophysical Journal on ...

Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...

What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better ...

With a strong start to fiscal 2026, NetraMark is reiterating its previously stated guidance of achieving C$8–$10 million in booked contract backlog by mid-2026. This outlook is ...

A research team introduces a hierarchical Bayesian spatial approach that integrates UAV and terrestrial LiDAR data to estimate AGB of individual trees in natural secondary forests of northeastern ...

Introduction Asthma is a chronic respiratory disorder requiring ongoing medical management. This ecological study investigated the spatial and temporal patterns of notification rates for asthma from ...

The research, published in Nature Communications, presents for the first time a detailed molecular portrait of neuronal connections that are key to memory. It demonstrates that, although they share ...

Abstract: Accurate cellular network traffic prediction is crucial for intelligent network planning and management in 6G. However, the non-stationary characteristics of cellular network traffic present ...