Workshop on Rethinking the Role of Bayesianism in the Age of Modern AI

2026-06-22 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

A workshop titled "Rethinking the Role of Bayesianism in the Age of Modern AI" will convene from October 26 to 30, 2026, in Edinburgh, Scotland, UK. Hosted by the University of Edinburgh's School of Informatics, this event aims to gather researchers at the forefront of Bayesian machine learning and deep learning. Organized by Esmeralda Whitammer, Sara Wade, Vincent Fortuin, Konstantina Palla, and Theodore Papamarkou, the workshop builds upon previous meetings held at Dagstuhl in 2024 and MBZUAI in 2025. This year, it gains official recognition as an event of the International Society for Bayesian Analysis (ISBA)'s new section on Bayesian AI. The program emphasizes collaboration and interaction, featuring talks, panel discussions, poster sessions, and dedicated time for participant engagement across diverse perspectives.

Key takeaway

For AI Scientists and Research Scientists focused on probabilistic modeling, consider attending the "Rethinking the Role of Bayesianism in the Age of Modern AI" workshop in October 2026. Your participation offers a direct opportunity to engage with leading experts, influence the direction of Bayesian AI research, and network within the International Society for Bayesian Analysis's new Bayesian AI section. This event is crucial for staying current and contributing to the integration of Bayesian methods into advanced deep learning.

Key insights

The workshop aims to advance Bayesian methods within modern AI, fostering collaboration among researchers.

Topics

• Bayesianism
• Machine Learning
• Deep Learning
• AI Research
• Probabilistic Modeling
• ISBA

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.