Episode description
Episode description
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
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Takeaways:
- Designing experiments is about optimal data gathering.
- The optimal design maximizes the amount of information.
- The best experiment reduces uncertainty the most.
- Computational challenges limit the feasibility of BED in practice.
- Amortized Bayesian inference can speed up computations.
- A good underlying model is crucial for effective BED.
- Adaptive experiments are more complex than static ones.
- The future of BED is promising with advancements in AI.
Chapters:
00:00 Introduction to Bayesian Experimental Design
07:51 Understanding Bayesian Experimental Design
19:58 Computational Challenges in Bayesian Experimental Design
28:47 Innovations in Bayesian Experimental Design
40:43 Practical Applications of Bayesian Experimental Design
52:12 Future of Bayesian Experimental Design
01:01:17 Real-World Applications and Impact
Thank you to my Patrons for making this episode possible!
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