Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference

Understand

Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods.

• While traditional statistical approaches like approximate Bayesian computation and Bayesian synthetic likelihood have been studied under well-specified and misspecified settings, they often suffer from inefficiencies due to wasted model simulations.
• Neural approaches, such as sequential neural likelihood (SNL) avoid this wastage by utilising all model simulations to train a neural surrogate for the likelihood function.
• However, the performance of SNL under model misspecification is unreliable and can result in overconfident posteriors centred around an inaccurate parameter estimate.