A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
This identifies a critical reliability issue in simulation-based inference, which could limit its use in scientific domains where accurate uncertainty quantification is essential.
The paper demonstrates that current Bayesian simulation-based inference algorithms often produce overconfident posterior approximations, making them unreliable for scientific applications, and suggests ensembling as a mitigation strategy.
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our results show that all benchmarked algorithms -- (Sequential) Neural Posterior Estimation, (Sequential) Neural Ratio Estimation, Sequential Neural Likelihood and variants of Approximate Bayesian Computation -- can yield overconfident posterior approximations, which makes them unreliable for scientific use cases and falsificationist inquiry. Failing to address this issue may reduce the range of applicability of simulation-based inference. For this reason, we argue that research efforts should be made towards theoretical and methodological developments of conservative approximate inference algorithms and present research directions towards this objective. In this regard, we show empirical evidence that ensembling posterior surrogates provides more reliable approximations and mitigates the issue.