Podcast coming on Bayesian workflow! With a contest!

2026-06-08 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Alexandre Andorra will host a podcast interview featuring Andrew Gelman, Aki Vehtari, and Richard McElreath to discuss their recently released book, "Bayesian Workflow". Concurrently, a contest is announced, offering listeners the chance to have their real-world workflow problem addressed live on the recording by the three authors. To enter, participants must like, comment, or repost the announcement, review the show on Apple Podcasts or Spotify, and submit a problem description (100-200 words) via a Google Form. The grand prize includes the live problem-solving session and a signed book, with two runners-up receiving signed copies and five LBS Patreon subscribers getting ebook copies. The contest concludes on June 10.

Key takeaway

For data scientists or research scientists applying Bayesian methods, this contest offers a rare chance to get direct expert input on your workflow challenges. If you are grappling with a specific problem, consider submitting your 100-200 word description by June 10. This is your opportunity to have Andrew Gelman, Aki Vehtari, and Richard McElreath analyze your issue live. This could provide invaluable insights for your projects.

Key insights

A podcast interview and contest offer a unique opportunity to engage with authors of the "Bayesian Workflow" book.

Method

Contest entry requires liking/reposting, reviewing the podcast on Apple Podcasts or Spotify, and submitting a 100-200 word problem via a Google Form.

In practice

• Submit a real-world workflow problem.
• Review the show on podcast platforms.
• Engage with the announcement post.

Topics

• Bayesian Workflow
• Podcast Interview
• Statistical Modeling
• Data Science Contest
• Expert Q&A

Best for: Data Scientist, Machine Learning Engineer, Research Scientist

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