Full day Stan tutorial at Modern Modeling Methods (M3) this summer in New York (22 June 2026)

2026-05-21 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Mathematics & Computational Sciences, Social Sciences & Behavioral Studies · Depth: Novice, quick

Summary

Stan developers Mitzi Morris and Bob Carpenter will present a full-day, hands-on tutorial on Stan and Bayesian data analysis at the Modern Modeling Methods (M3) Conference in New York, held at Fordham University Lincoln Center Campus from June 22–24, 2026. Scheduled for June 22, this workshop targets psychometricians, introducing them to Bayesian modeling and statistical inference using the probabilistic programming language Stan. The curriculum will cover key Bayesian properties, using Stan for model coding and inference, full Bayesian posterior inference including calibration and model comparison, and translating structural equation models (SEM) to Stan. It will also introduce psychological models for educational testing, crowdsourcing, rating/ranking, and real-time decision processes. Participants will utilize the browser-based Stan Playground, with potential demonstrations in R or Python for advanced methods like brms.

Key takeaway

For psychometricians or research scientists seeking to deepen their quantitative modeling skills, attending the Stan tutorial at M3 on June 22, 2026, offers a direct path to hands-on Bayesian data analysis. You will learn to translate complex structural equation models and psychological models into Stan, gaining practical experience in full Bayesian posterior inference. Consider this workshop to enhance your ability to model uncertainty and leverage existing knowledge in your research, using tools like the Stan Playground.

Key insights

The tutorial offers a hands-on introduction to Bayesian modeling and inference with Stan, tailored for psychometric applications.

Principles

• Bayesian modeling integrates uncertainty and prior knowledge.
• Generative processes define data generating distributions.
• Full Bayesian posterior inference includes calibration.

Method

The tutorial uses Stan to code models and perform statistical inference, covering retrospective parameter estimates, prospective predictions, and model comparison with cross-validation. It translates structural equation models (SEM) to Stan.

In practice

• Code psychological models in Stan.
• Perform inference for educational testing.
• Analyze crowdsourcing and rating data.

Topics

• Stan
• Bayesian Modeling
• Psychometrics
• Statistical Inference
• Structural Equation Models
• Probabilistic Programming

Best for: Research Scientist, Data Scientist, AI Student

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