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Open source made MMM cheaper, not easier

Free tools have lowered the cost of marketing mix modeling, but data quality and human expertise remain the biggest barriers ...

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 ...
GitHub

StatsPAI: an Agent&Python-native Stata/R replacement for applied causal inference

StatsPAI is for empirical researchers who would normally jump between Stata, R, and Python. Its goal is to make common Stata/R econometrics and causal-inference workflows feel native in Python: load a ...
Phys.org

Researchers develop Bayesian inference for hidden dependence structures in multi-group high-dimensional data

In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...
IEEE

A Unified Bayesian Framework for Topology Inference, Blind System Identification, and Source Localization in Edge-Cloud LAWNs

Abstract: Distributed data analysis tasks-including topology inference, blind system identification, and source localization-are critical for Low-Altitude Wireless Networks (LAWNs) architected on an ...
GitHub

T-rate: Bayesian Inference of Stress Evolution From Seismicity Rate Observations

T-rate is a Python/PyMC tool for inferring transient stress evolution from changes in seismicity rate. It was developed for seismicity rate observations governed by rate-and-state friction and is ...
Hacker

OpenAI Privacy Filter Brings Long-Context PII Masking to Text

privacy-filter is a bidirectional token-classification model for detecting and masking personally identifiable information (PII) in text, developed by openai. The model uses a pre-norm transformer ...
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 ...
PNAS

Free information disrupts even Bayesian crowds

A foundational assumption in the design of digital communication platforms is that information sharing is always beneficial. Using an agent-based model of truth-seeking and cooperative agents with ...
Forbes

AI Inference Takes Center Stage At KubeCon Europe 2026

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. KubeCon + CloudNativeCon Europe 2026 in Amsterdam made one thing clear. Kubernetes is no ...
TechCrunch

Startup Gimlet Labs is solving the AI inference bottleneck in a surprisingly elegant way

Stanford adjunct professor and successfully exited founder Zain Asgar just raised an $80 million Series A for a startup that solve the AI inference bottleneck problem in an astute way. The round was ...