What an Amortized X-ray Posterior Cannot See: Gain Shifts, Silent Miscalibration, and Where Nested Sampling Still Earns Its Cost
This paper benchmarks neural posterior estimation against nested sampling for X-ray spectral analysis, demonstrating that while amortized methods offer speed, they require specific trust diagnostics like posterior-predictive checks and evidence-based model comparison to detect silent miscalibration, gain shifts, and unmodeled features that standard recovery metrics miss.