Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes
Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows. We consider learning sufficient summary statistics jointly with the posterior as a general principle for scalable inference in various SBI methods.
This paper introduces a method for improving Bayesian simulation-based inference (SBI) by simultaneously learning summary statistics and posterior distributions, addressing challenges in scalability with large datasets.
What happened
The article presents a novel approach to Bayesian simulation-based inference where sufficient summary statistics are learned simultaneously with the posterior distribution. This method uses a two-way guidance mechanism to improve scalability, demonstrating that structural constraints on summaries can enhance rather than limit inference performance.
Why it matters
For AI developers and researchers, this work addresses a critical challenge in probabilistic modeling: efficiently handling large datasets while maintaining accurate posterior estimation. The method could improve the practicality of SBI in real-world applications requiring scalable inference.
- Joint learning of summaries and posteriors improves SBI scalability
- Structural constraints on summaries can enhance inference performance
- The two-way guidance mechanism provides theoretical guarantees for accuracy
- This method addresses a key limitation in handling concentrated posteriors
The bigger picture
The approach could enable more efficient and accurate probabilistic inference in domains requiring large-scale data processing, such as computational biology, physics simulations, and complex system modeling.
The paper introduces a theoretically grounded approach to Bayesian simulation-based inference that simultaneously learns summary statistics and posterior distributions, offering improved scalability for large datasets through a novel two-way guidance mechanism.
Technical details
- Bayesian simulation-based inference (SBI)
- Sufficient summary statistics
- Two-way guidance mechanism
- Posterior sufficiency
- Structural restrictions on summaries
FAQ
What problem does this research address?
The research addresses the challenge of maintaining accurate Bayesian inference as dataset sizes grow, particularly with the difficulty of recovering concentrated posteriors in simulation-based inference.
What is 'two-way guidance'?
Two-way guidance is a mechanism where structural constraints on summary statistics improve posterior learning, while posterior objectives guide summaries toward sufficiency, creating a mutually reinforcing relationship.
Why are structural restrictions on summaries beneficial?
Structural restrictions simplify posterior learning and help summaries achieve sufficiency, counterintuitively improving performance rather than limiting it.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - stat.ML on Oct 12, 2026.
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