Neural Posterior Regularization for Likelihood-Free Inference
This work addresses a domain-specific problem for researchers and practitioners in simulation-based inference, offering an incremental improvement over existing Bayesian methods.
The paper tackles the challenge of Bayesian inference in simulation-based tasks with multi-modal, high-dimensional outputs by introducing Neural Posterior Regularization (NPR), which enforces effective exploration of input parameter space and provides a closed-form solution for analysis, resulting in statistically significant gains on benchmark performances across diverse simulation tasks.
A simulation is useful when the phenomenon of interest is either expensive to regenerate or irreproducible with the same context. Recently, Bayesian inference on the distribution of the simulation input parameter has been implemented sequentially to minimize the required simulation budget for the task of simulation validation to the real-world. However, the Bayesian inference is still challenging when the ground-truth posterior is multi-modal with a high-dimensional simulation output. This paper introduces a regularization technique, namely Neural Posterior Regularization (NPR), which enforces the model to explore the input parameter space effectively. Afterward, we provide the closed-form solution of the regularized optimization that enables analyzing the effect of the regularization. We empirically validate that NPR attains the statistically significant gain on benchmark performances for diverse simulation tasks.