The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches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

The goal of neurogam is to provide utilities for estimating the onset and offset of time-resolved effects, such as those found in M/EEG, pupillometry, or finger/mouse-tracking data (amongst others).

The jump to marketing mix modeling-like features for small companies became a reality with support from AI agents, but ...

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 ...

This article explores the potential of large language models (LLMs) in reliability systems engineering, highlighting their applications in improving industrial efficiency and flexibility. It also ...

A collaborative study, which includes contributions from NDORMS researchers Adam Crowther and supervisor Dario Carugo, explores new ways to model bladder biology. Urinary tract infections (UTIs) ...

A production-ready stock price prediction system using Bayesian Optimized LSTM models with advanced uncertainty quantification and multi-step forecasting capabilities. Stock_Price_Prediction/ ├── 🎯 ...

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 ...

Google Translate is adding a model picker that lets users choose between "Fast" and a new "Advanced" translation option. The new picker appears beneath the Google Translate logo at the top of the app ...