In 2025, large language models moved beyond benchmarks to efficiency, reliability, and integration, reshaping how AI is ...

We are living at a time when large language models increasingly make choices once reserved for people. From writing emails to ...

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.

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: This study examines logit models applied to the truck route choice problem using GPS trucking data from the Dallas metropolitan area. Instead of assuming a constant coefficient for each ...

A study conducted by experts from the University of the Philippines-Diliman showed that logistic regression is a reliable ...

Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...

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

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 ...

Abstract: Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of ...

An analysis by Shinshu University and the Distribution Economics Institute of Japan shows that wholesale prices for ...