Bayesian Causal Networks in R
7–11 September 2026
Online (live sessions + recordings for global participation)
Bayesian causal networks (Bayesian belief networks) are probabilistic graphical models used to represent complex systems under uncertainty. They combine causal structure (DAGs) with Bayesian inference for prediction, reasoning, and decision support.
This course provides a practical introduction to building and analysing Bayesian causal networks in R, with emphasis on:
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Directed acyclic graphs (DAGs) and causal assumptions
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Structure learning and model averaging
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Parameter learning (conditional probability tables)
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Posterior inference and scenario analysis
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Model validation and sensitivity analysis
Participants will build and evaluate a full Bayesian network (approximately 10+ variables) using real and simulated datasets.
Target audience
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PhD students and advanced Master’s students
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Researchers and practitioners working with probabilistic models
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Anyone interested in causal inference and graphical models
Format
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5 interactive live sessions (3 hours each, 15:00–18:00 Berlin time)
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Conceptual lectures and live coding in R
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Guided hands-on exercises
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Additional self-paced materials with support
More information: Bayesian Causal Networks in R - physalia-courses
We would be happy if this is of interest to the Stan/Bayesian modeling community.