Abstract
Simulation-based inference (SBI) has emerged as a powerful framework for performing Bayesian inference when likelihood functions are intractable. However, there remains a significant gap between methodological advances and practical deployment. In this talk, I discuss:
- Current State of SBI Methods: Overview of neural posterior/likelihood/ratio estimation approaches
- The Applicability Gap: Why aren’t more practitioners using SBI?
- Software Solutions: How the sbi package addresses usability challenges
- Real-world Case Studies: Applications in neuroscience, epidemiology, and climate science
- Future Directions: Scaling SBI to higher dimensions and real-time applications
Key Takeaways
- SBI methods are mature enough for practical deployment
- Software accessibility is crucial for adoption
- Domain-specific customization often required
- Diagnostics and validation remain challenging
Venue
TransferLab Internal Seminar Series
appliedAI Institute for Europe
Munich, Germany