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 ...

The real-world data of power networks is often inaccessible due to privacy and security concerns, highlighting the need for tools to generate realistic synthetic network data. Existing methods ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Every Tuesday at 11:05 a.m., students, faculty, staff and the greater BYU community attend the weekly devotional or forum address in the Marriott Center.

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

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 ...

Researchers from BI Norwegian Business School and NHH Norwegian School of Economics have developed a new behavioral credit-risk model that integrates ...

The universe is expanding, but astronomers still cannot agree on exactly how fast. That mismatch, known as the Hubble tension, has turned a seemingly simple number into one of the biggest headaches in ...

The hierarchical flow for clock domain crossing (CDC) and reset domain crossing (RDC) is a methodology used in the verification of large, complex digital integrated circuits. It’s a divide-and-conquer ...

Brain activities often follow an exponential family of distributions. The exponential distribution is the maximum entropy distribution of continuous random variables in the presence of a mean. The ...

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 ...