Mining gold from implicit models to improve likelihood-free inference
This addresses the challenge of likelihood-free inference for researchers using complex simulators in fields like physics or biology, though it appears incremental as an extension of existing neural network-based methods.
The paper tackles the inverse problem in simulation-based inference where implicit models have intractable densities, by developing techniques that extract additional information like joint likelihood ratios and scores from simulators to augment training data for neural network surrogate models. The result is more sample-efficient and higher-fidelity inference compared to traditional methods.
Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.