Efficient and reliable handling of model misspecification in Neural Posterior Estimation

Develop efficient and reliable methods to address model misspecification in Neural Posterior Estimation (NPE), ensuring accurate posterior inference when the data-generating process cannot reproduce observed data characteristics. The approach should provide robustness to misspecification without sacrificing computational efficiency in realistic biological applications.

Background

NPE and its sequential variants (SNPE) typically assume the underlying simulator can replicate observations. In real-world applications this assumption often fails due to model misspecification, leading to poor posterior approximations. Although recent work explores robustness in NPE, a general solution is lacking.

The authors stress that existing efforts have not produced an approach that is simultaneously efficient and reliable, underscoring the need for methods that remain accurate under misspecification while maintaining the simulation-efficiency advantages of neural SBI.