Recreating Michael Betancourt’s Bayesian modeling course from his online materials

[This article was first published on Shravan Vasishth's Slog (Statistics blog), and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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Several people wanted to have the slides from Betancourt’s lectures at SMLP2018. It is possible to recreate most of the course from his writings:

1. Intro to probability:
https://betanalpha.github.io/assets/case_studies/probability_theory.html

2. Workflow:
https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

3. Diagnosis:
https://betanalpha.github.io/assets/case_studies/divergences_and_bias.html

4. HMC: https://www.youtube.com/watch?v=jUSZboSq1zg


5. Validating inference: https://arxiv.org/abs/1804.06788

6. Calibrating inference: https://arxiv.org/abs/1803.08393

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