Abstract: The rapid expansion of large language models (LLMs) has led to increasingly frequent interactions between LLM agents and human users, motivating new questions about their capacity to form ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

These open-source MMM tools solve different measurement problems, from budget optimization to forecasting and preprocessing.

In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a ...

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...

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 ...

ABSTRACT: This study presents the Dynamic Multi-Objective Uncapacitated Facility Location Problem (DMUFLP) model, a novel and forward-thinking approach designed to enhance facility location decisions ...

Abstract: In this paper, a new extension of the Rayleigh-Weibull (RW) model, called the sine Rayleigh-Weibull (SRW) model, is proposed. This model is constructed using the trigonometrically generated ...

Verses demonstrates progress in leveraging AI models using Bayesian networks and active inference that are significantly smaller, more energy efficient, and honest than Deep Neural Network approaches.