The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches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

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

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

In production engineering, the identification of optimal process parameters is essential to advance product quality and overall equipment effectiveness. Optimizing and adapting process parameters ...

Today at CES, Slingwave unveiled its unified, AI-native platform for growing eCommerce and direct-to-consumer brands that helps marketers make the right spending decisions to grow their brand. The ...

Abstract: Parallel Bayesian optimization is crucial for solving expensive black-box problems, yet batch acquisition strategies remain a challenge. To address this, we propose a novel parallel ...

Abstract: This article proposes a novel constrained multiobjective evolutionary Bayesian optimization algorithm based on decomposition (named CMOEBO/D) for expensive constrained multiobjective ...