Why marketing mix modeling is still hard to get right

Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...
Eos

Comparing Machine Learning Models of Raindrop Formation

Better simulations of raindrop formation could help improve climate and weather models. This newsletter rocks. Get the most ...
techtimes

Microsoft Build 2026: MAI-Thinking-1 Is First In-House Reasoning Model, Trained Without OpenAI Data

shows the logo of Microsoft displayed on the facade of the company France headquarters in Issy-les-Moulineaux, on the outskirts of Paris, on April 24, 2026. Martin LELIEVRE/Getty Images Microsoft ...
VentureBeat

MiniMax teases upcoming M3 model with new sparse attention mechanism and 15.6X long-context response speed boost

Among the many Chinese AI companies and laboratories vying for market share and attention (no pun intended) on the global marketplace, MiniMax stands out for its commitment to providing frontier-level ...
The American Journal of Managed Care

Food Insecurity Identification Modeling for Medicare Enrollees Using Administrative Data

Food insecurity identification modeling for Medicare can establish a reliable method of prioritizing members at risk of food insecurity for identification and program enrollment. Objectives: Food ...
The New York Times

White House Considers Vetting A.I. Models Before They Are Released

The Trump administration, which took a noninterventionist approach to artificial intelligence, is now discussing imposing oversight on A.I. models before they are made publicly available. By Tripp ...
ascopubs.org

Canine Olfaction Combined With Bayesian Modeling for Multicancer Detection From Breath Samples: A Phase II Study in India

Circulating Tumor DNA Assessment of Disease Response in Large B-Cell Lymphoma: Lisocabtagene Maraleucel Versus Autologous Stem Cell Transplantation Standard Therapy We conducted an assessor-masked, ...
Frontiers

Null hypothesis significance testing vs. Bayesian inference using generalized linear mixed models with binary outcomes: a case study under practical design constraints

Empirical investigation requires dealing with fundamental uncertainty. In experimental psychology, research questions are often addressed using Null Hypothesis Significance Testing (NHST), an approach ...
IEEE

Gaussian Mixture Model Based Bayesian Learning for Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated Systems

Abstract: A novel Gaussian mixture model (GMM)–aided sparse Bayesian learning (SBL) framework is proposed for channel state information (CSI) estimation in orthogonal time-frequency space (OTFS) ...
International Monetary Fund

Nowcasting Growth Using the Bayesian Structural Time Series Model: Application to Tanzania

In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term economic fluctuations, especially in countries with limited access to ...
VentureBeat

Qwen3-Coder-Next offers vibe coders a powerful open source, ultra-sparse model with 10x higher throughput for repo tasks

Chinese e-commerce giant Alibaba's Qwen team of AI researchers has emerged in the last year as one of the global leaders of open source AI development, releasing a host of powerful large language ...