#147 Fast Approximate Inference without Convergence Worries, with Martin Ingram

Learning Bayesian Statistics

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Takeaways:

• DADVI is a new approach to variational inference that aims to improve speed and accuracy.
• DADVI allows for faster Bayesian inference without sacrificing model flexibility.
• Linear response can help recover covariance estimates from mean estimates.
• DADVI performs well in mixed models and hierarchical structures.
• Normalizing flows present an interesting avenue for enhancing variational inference.
• DADVI can handle large datasets effectively, improving predictive performance.
• Future enhancements for DADVI may include GPU support and linear response integration.

Chapters:

13:17 Understanding DADVI: A New Approach

21:54 Mean Field Variational Inference Explained

26:38 Linear Response and Covariance Estimation

31:21 Deterministic vs Stochastic Optimization in DADVI

35:00 Understanding DADVI and Its Optimization Landscape

37:59 Theoretical Insights and Practical Applications of DADVI

42:12 Comparative Performance of DADVI in Real Applications

45:03 Challenges and Effectiveness of DADVI in Various Models

48:51 Exploring Future Directions for Variational Inference

53:04 Final Thoughts and Advice for Practitioners

Thank you to my Patrons for making this episode possible!

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