Скачать или смотреть Bayesian Reasoning in Practice [1/3]: A Gentle Introduction to Bayesian Workflow (H. Benali)

Bayesian Reasoning in Practice: Intuitive, Powerful, and Immediately Useful
Session 1 of 3 | A Gentle Introduction to Bayesian Workflow

This video is the first session in a three-part seminar series designed to introduce the logic and practical application of Bayesian Workflow in scientific research. The series is part of the ⁨Open Research Methods Exchange, a platform for hands-on, interdisciplinary training in research methodology.

TIMESTAMPS
00:00:00 Motivation and Structure of the Series
00:06:47 Outline
00:07:55 Conceptual Introduction
00:35:33 Bayesian Modelling
01:11:12 Regression Model: Example
01:21:24 Characterising the Posterior Distribution
02:08:26 Summary, Conclusion, and Next Steps
02:10:43 Who Discovered Bayes' Theorem?

OVERVIEW
This session introduces key practical concepts of the Bayesian workflow most useful for researchers. Topics include a conceptual introduction to Bayesian modelling, an example using regression, and tools for characterising and interpreting the posterior distribution.

TARGET AUDIENCE
Students, engineers, and researchers seeking to strengthen their understanding of statistical inference. No prerequisites are required, though prior exposure to exploratory and inferential statistics is an asset.

UPCOMING SESSIONS
Session 2: Hands-on introduction to software for Bayesian modelling (May 2025)
Session 3: Full Bayesian workflow on real-world data using R–Stan (June 2025)

INSTRUCTOR
Dr. Habib Benali (Department of Electrical and Computer Engineering, Concordia University)
Delivered live on April 16, 2025 (hybrid: Zoom + in-person at Concordia SGW campus)

FURTHER READING AND REFERENCES
Barthoulot & Jaksic (2024). Intro à la statistique bayésienne. HUG/UNIGE.
(https://www.hug.ch/sites/interhug/fil...)
Makowski et al. (2019). Indices of effect in Bayesian framework. Front. Psychol.
(https://doi.org/10.3389/fpsyg.2019.02767)
Van de Schoot et al. (2014). A gentle intro to Bayesian analysis. Child Dev.
(https://doi.org/10.1111/cdev.12169)
O'Hagan (2008). The Bayesian approach to statistics.
(https://maths.nuigalway.ie/~milovan/1...)
Proctor (n.d.). R course: Bayesian modelling.
(https://andrewproctor.github.io/rcour...)
easystats team (n.d.). bayestestR documentation.
(https://easystats.github.io/bayestest...)
Stigler (1983). Who discovered Bayes's theorem? Am. Stat.
(https://doi.org/10.1080/00031305.1983...)

ORGANISED BY
Biomedical Imaging and Healthy Aging Laboratory
Part of the ‪@OpenResearchMethodsExchange‬ training initiative

For updates and materials: [Link to come]

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