
#133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt
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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
- Intro to Bayes Course (first 2 lessons free)
- Advanced Regression Course (first 2 lessons free)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
- Zero Sum constraints allow for better sampling and estimation in hierarchical models.
- Understanding the difference between population and sample means is crucial.
- A library for zero-sum normal effects would be beneficial.
- Practical solutions can yield decent predictions even with limitations.
- Cholesky parameterization can be adapted for positive correlation matrices.
- Understanding the geometry of sampling spaces is crucial.
- The relationship between eigenvalues and sampling is complex.
- Collaboration and sharing knowledge enhance research outcomes.
- Innovative approaches can simplify complex statistical problems.
Chapters:
03:35 Sean Pinkney's Journey to Bayesian Modeling
11:21 The Zero-Sum Normal Project Explained
18:52 Technical Insights on Zero-Sum Constraints
32:04 Handling New Elements in Bayesian Models
36:19 Understanding Population Parameters and Predictions
49:11 Exploring Flexible Cholesky Parameterization
01:07:23 Closing Thoughts and Future Directions
Thank you to my Patrons for making this episode possible!
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